Note: Descriptions are shown in the official language in which they were submitted.
METHOD AND DEVICE FOR VOICE INFORMATION VERIFICATION,
ELECTRONIC EQUIPMENT AND STORAGE MEDIUM
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of artificial
intelligence,
particularly to a voice information verification method, a voice information
verification
apparatus, an electronic device and a computer readable memory medium.
BACKGROUND ART
[0002] With the development of computer technology, on many Apps
(Applications) and
websites, malicious behaviors such as stealing of accounts, fake accounts, and
online scams have
appeared, which pose a security risk to the normal operation of the Apps and
the websites.
Therefore, it is necessary to verify identity of every kind of account to stop
stolen accounts or
fake accounts from committing network misconducts and safeguard the interests
of real users.
[0003] It needs to be explained that information disclosed in the foregoing
section of
background art is only intended to deepen understanding on the background of
the present
disclosure, so it may include information that does not constitute the prior
art known to those of
ordinary skill in the art.
SUMMARY
[0004] The present disclosure provides a voice information verification
method, a voice
information verification apparatus, an electronic device and a computer
readable memory
medium, thereby overcoming at least to some extent the problem of low security
of identity
verification owing to defects of the prior art.
[0005] Other features and advantages of the present disclosure will be
evident through the
following detailed description, or partially learnt through practice of the
present disclosure.
[0006] According to one aspect of the present disclosure, a voice
information verification
method is provided, comprising: obtaining a verification code and a voice to
be verified sent by a
user terminal regarding the verification code; judging whether the voice to be
verified is of
non-machine or not in response to match between the voice to be verified and
the verification
code; and judging that the voice to be verified has passed the verification if
the voice to be
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verified is of non-machine.
[0007] In an exemplary embodiment of the present disclosure, judging
whether the voice to
be verified is of non-machine or not if the voice to be verified is matched
with the verification
code comprises: converting the voice to be verified into a target spectrogram
if the voice to be
verified is matched with the verification code; analyzing the target
spectrogram by a
convolutional neural network model to obtain a man machine classification
result of the target
spectrogram; and determining whether the voice to be verified is of non-
machine or not based on
the man machine classification result.
[0008] In an exemplary embodiment of the present disclosure, obtaining a
verification code
comprises: obtaining a verification code that has undergone disturbance
processing.
[0009] In an exemplary embodiment of the present disclosure, obtaining a
verification code
that has undergone disturbance processing comprises: obtaining a preset text
and converting the
preset text into a target picture; and processing the target picture by one or
more of deformation,
discoloring, fuzzification and increase of noisy points to generate an image
verification code that
has undergone disturbance processing.
[0010] In an exemplary embodiment of the present disclosure, before
response to match
between the voice to be verified and the verification code, the method further
comprises:
detecting a length of the voice to be verified; judging the voice to be
verified is failed in the
verification and returning a failure prompt message to the user terminal if
the length of the voice
to be verified is smaller than a preset length; and converting the voice to be
verified into a text to
be verified and matching the text to be verified with the verification code if
the length of the
voice to be verified is greater than or equal to a preset length.
[0011] In an exemplary embodiment of the present disclosure, converting the
voice to be
verified into a text to be verified comprises: pre-processing the voice to be
verified by one or
more of sound track conversion, pre-emphasis, speech enhancement and blank
deletion; and
converting the pre-processed voice to be verified into the text to be verified
by a time delay
neural network model.
[0012] In an exemplary embodiment of the present disclosure, the
verification code
comprises a text verification code, matching the text to be verified with the
verification code
comprises: matching the text to be verified with the text verification code to
obtain a typo
proportion of the text to be verified; and judging whether the voice to be
verified is of
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non-machine or not if the voice to be verified is matched with the
verification code comprises:
judging whether the voice to be verified is of non-machine or not if the typo
proportion is lower
than a threshold of matching.
[0013] According to one aspect of the present disclosure, a voice
information verification
apparatus is provided, comprising: an information obtaining module, for
obtaining a verification
code and a voice to be verified sent by a user terminal regarding the
verification code; a man
machine judgment module, for judging whether the voice to be verified is of
non-machine or not
in response to match between the voice to be verified and the verification
code; and a voice
verification module, for determining that the voice to be verified has passed
the verification if the
voice to be verified is of non-machine
[0014] According to one aspect of the present disclosure, an electronic
device is provided,
comprising: a processor; and a memory, for storing executable instructions of
the processor;
wherein the processor is configured to execute the method in any of the
foregoing descriptions
by executing the executable instructions.
[0015] According to one aspect of the present disclosure, a computer
readable memory
medium is provided, and stores a computer program. When being executed by a
processor, the
computer program achieves the method in any of the foregoing descriptions.
[0016] The exemplary embodiments of the present disclosure have the
following beneficial
effects:
[0017] A verification code is matched with a voice to be verified sent by a
user terminal
regarding the verification code. Whether the successfully matched voice to be
verified is of
non-machine or not is judged. If the judgment result is of non-machine, a
verification result of
the voice to be verified is obtained. On the one hand, match between a voice
to be verified and a
verification code and verification of man machine judgment can verify
consistency of user
identity and meanwhile, reduce attacks of machine synthesized voices at a
system and improve
security of the voice information verification method. On the other hand, in
this exemplary
embodiment, the user neither needs to input voice registration information in
advance nor needs
to store user's voice print characteristic information, thereby reducing use
cost of the voice
information verification method, simplifying user's operation flow, reducing
resource occupation
of the system and raising efficiency.
[0018] It should be understood that the foregoing general description and
subsequent
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detailed description are only exemplary and explanatory and cannot limit the
present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings here are incorporated into the
Description, form a part
of the Description, show embodiments that conform to the present disclosure
and are used
together with the Description to explain the theory of the present disclosure.
Apparently,
accompanying drawings in subsequent description are only some embodiments of
the present
disclosure, and those of ordinary skill in the art can obtain other
accompanying drawings
according to these accompanying drawings without creative work.
[0020] FIG. 1 shows a schematic diagram of system architecture of an
operating
environment of this exemplary embodiment;
[0021] FIG. 2 schematically shows a step diagram of a voice information
verification
method in this exemplary embodiment;
[0022] FIG. 3 schematically shows a sub-flow chart of a voice information
verification
method in this exemplary embodiment;
[0023] FIG. 4 schematically shows a flow chart of a method for obtaining a
verification
code in this exemplary embodiment;
[0024] FIG. 5 schematically shows a sub-flow chart of an alternative voice
information
verification method in this exemplary embodiment;
[0025] FIG. 6 schematically shows a flow chart of a voice information
verification method
in this exemplary embodiment;
[0026] FIG. 7 schematically shows a structural block diagram of a voice
information
verification apparatus in this exemplary embodiment;
[0027] FIG. 8 schematically shows an electronic device for achieving the
foregoing method
in this exemplary embodiment;
[0028] FIG. 9 schematically shows a computer readable memory medium for
achieving the
foregoing method in this exemplary embodiment.
DETAILED DESCRIPTION
[0029] Now exemplary implementation manners are more comprehensively
described with
reference to accompanying drawings. However, exemplary implementation manners
can be
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implemented in various forms and should not be understood that they are
limited to the examples
set forth herein; on the contrary, provision of these implementation manners
make the present
disclosure more comprehensive and complete and comprehensively conveys the
conception of
the exemplary implementation manners to those skilled in the art. The
described characteristics,
structures or features can be combined in one or more implementation manners
in any
appropriate way.
[0030] In a solution of the prior art, voice verification is conducted by
matching user's
own voice print characteristics. The user needs to input voice registration
information during
registration from which voice print information is extracted so that during
logon of the user in
the future, the voice verification information is matched with the voice print
characteristics.
However, in this solution, if the voice registration information or voice
verification information
is synthesized by a machine, for example, voice verification information
synthesized using
original voices of a user after daily voice information of the user is
crawled, the server can hardly
recognize it. What is more, user's voice print characteristic information
needs to be stored,
increasing resource occupation of the system.
[0031] In view of the foregoing problem, an exemplary embodiment of the
present
disclosure firstly provides a voice information verification method. The voice
information
verification method can be applied in a scenario of verifying the identity of
a user by a
verification code when the user logs on an App or web pages, or confirms a
payment, or modifies
a password or carries out other sensitive operations.
[0032] FIG. 1 shows a schematic diagram of system architecture of an
operating
environment of this exemplary embodiment. As shown in FIG. 1, this system 110
may comprise
a user terminal 111, a network 112 and a server 113. Here, the user terminal
111 can be various
kinds of terminal devices used by the user such as personal computer, tablet
computer, smart
phone or wearable device, and sends a collected voice to be verified regarding
a verification code
to the server 113 via the network 112; the server 113 can obtain the voice to
be verified from the
user terminal 111 and perform voice information verification of the voice to
be verified.
[0033] It should be understood that the number of devices shown in FIG. 1
is exemplary
only. According to the actual needs, any number of the user terminals 111 or
the networks 112
can be set and the server 113 can also be a server cluster composed of a
plurality of servers.
[0034] Based on the foregoing description, the method in this exemplary
embodiment can be
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applied on the server 113 shown in FIG. 1.
[0035] Below, this exemplary embodiment is further described with reference
to FIG. 2. As
shown in FIG. 2, the voice information verification method may comprise the
following steps
S210¨S250:
[0036] Step S210, obtaining a verification code as well as a voice to be
verified sent by a
user terminal regarding the verification code.
[0037] Here, the verification code can be a text verification code, or a
voice verification
code, or an image verification code, etc., and can be generated from a
specific program of a
server, or obtained from other servers. The voice to be verified is a voice
sent by a user according
to the content of a verification code, such as a voice recorded and uploaded
when the user reads
the content of the verification code. The user terminal can collect a voice to
be verified regarding
a verification code through trigger operation of a user, and then send the
voice to be verified to a
server. For example, when a voice to be verified is input, a user can click on
a specific control to
input this voice to be verified, or select an existing sound recording file as
a voice to be verified.
[0038] Step S220, judging whether the voice to be verified is of non-
machine or not in
response to match between the voice to be verified and the verification code.
[0039] In this exemplary embodiment, firstly, whether the voice to be
verified is matched
with the verification code can be detected. For various types of verification
codes, there may be a
plurality of methods for matching a voice to be verified with a verification
code. For example, if
the verification code is a text verification code, matching can be conducted
by comparison with
the text in the text verification code; if the verification code is a voice
verification code,
matching can be conducted by comparing the voice verification code with the
voice print
characteristics of the voice to be verified. At Step S220, match between the
voice to be verified
and the verification code is equivalent to the primary verification, is mainly
used to verify
identity consistency and can recognize the circumstances where user's account
is stolen and is
logged in or operated on a terminal other than the bundled terminal.
[0040] In this exemplary embodiment, if the foregoing primary verification
is successful,
then the voice to be verified is subjected to man machine judgment, which is
called the
secondary judgment. When whether a voice to be verified is of a machine or non-
machine is
judged, the voice print characteristics of the voice to be verified can be
analyzed and matched,
and compared in whether the voice print characteristics are same as or similar
to the voice print
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characteristics of a machine synthesized sound, or whether the voice print
characteristics are
matched with the voice print characteristics in user's sound database.
[0041] Step S230, determining that the voice to be verified has passed the
verification if the
voice to be verified is of non-machine.
[0042] The secondary verification of a voice to be verified can eliminate
machine
synthesized voices, thereby determining that the user sending the voice to be
verified has
received a correct verification code and is a true user and it can be
determined that the voice to
be verified eventually passes verification.
[0043] In an exemplary embodiment, step S230 may comprise the following
steps:
[0044] converting the voice to be verified into a target spectrogram if the
voice to be verified
is matched with the verification code;
[0045] analyzing the target spectrogram by a convolutional neural network
model to obtain a
man machine classification result of the target spectrogram; and
[0046] determining whether the voice to be verified is of non-machine or
not based on the
man machine classification result.
[0047] The target spectrogram is a spectrogram corresponding to the voice
to be verified.
Typically there are two types of spectrograms: the first is an instantaneous
spectrogram with
frequency as x-coordinate and signal energy as y-coordinate, and a voice to be
verified can be
converted into a sequence of a plurality of instantaneous spectrograms; the
other is a continuous
spectrogram with time as x-coordinate and superposition frequency as y-
coordinate, and a voice
to be verified can be converted into a continuous spectrogram. After a server
obtains a voice to
be verified, the server can convert the voice to be verified into a voice file
in an appropriate
format, perform time domain analysis or frequency domain analysis of the voice
file, and draw a
target spectrogram. Apparently, the target spectrogram contains spectrum
features of the voice to
be verified, while the spectrum features can give expression to voice print
characteristics of the
voice to be verified. Therefore, through processing of a convolutional neural
network model,
voice print characteristics can be recognized, and are compared with voice
print characteristics of
machine synthesized voices learnt during model training or voice print
characteristics of real
human voice, and whether the target spectrogram is of machine or non-machine,
i.e., the man
machine classification result of the voice to be verified, is judged, thereby
completing a process
of the secondary verification.
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[0048] FIG. 3 shows a flow chart of a voice information verification method
in this
exemplary embodiment. As shown in FIG. 3, after a voice to be verified is
obtained, matching
with a verification code can be conducted at first. If the matching is failed,
a result of verification
failure can be output directly. If the matching is successful, the voice to be
verified will be
converted into a target spectrogram, then a machine judgment probability of
the target
spectrogram is output according to a convolutional neural network, and whether
the probability
is greater than a probability threshold is judged. If the probability is
greater than the probability
threshold, verification is failed; if the probability is not greater than the
probability threshold, a
result of final success in verification can be output.
[0049] Based on the foregoing description, this exemplary embodiment
matches a
verification code with a voice to be verified sent by a user terminal
regarding the verification
code, judges whether the successfully matched voice to be verified is of non-
machine or not, and
obtains a verification result of the voice to be verified if the judgment
result is of non-machine.
On the one hand, match between a voice to be verified and a verification code
and verification of
man machine judgment can verify consistency of user identity and meanwhile,
reduce attacks of
machine synthesized voices at a system and improve security of the voice
information
verification method. On the other hand, in this exemplary embodiment, the user
neither needs to
input voice registration information in advance nor needs to store user's
voice print characteristic
information, thereby reducing use cost of the voice information verification
method, simplifying
user's operation flow, reducing resource occupation of the system and raising
efficiency.
[0050] In an exemplary embodiment, the foregoing convolutional neural
network model can
implement training through the following steps:
[0051] obtaining a plurality of sample voices and category tags of the
sample voices.
[0052] converting the sample voices into sample spectrograms.
[0053] carrying out training using the sample spectrograms and the category
tags, and
obtaining a convolutional neural network model.
[0054] Here, the sample voices can be historical verification voices, and
may also include
some machine synthesized voices; category tags can be manually labeled tags
indicating each
sample voice is of "machine" or "non-machine".
[0055] The convolutional neural network model inputs sample spectrograms,
and outputs
classification results of the sample spectrograms. By adjusting model
parameters, outputted
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classification results can be more and more approximate to the category tags.
During training, the
sample spectrograms and the category tags can be divided into a training set
and a verification
set (e.g., at a ratio of 8:2). Here, the training set is used to train models
and set an initial learning
rate as well as preset learning times and percentage of reduction of learning
rate and can make a
model converge faster; the verification set is used to verify a training
effect of a model. If the
operating accuracy of the model on the verification set is up to a specific
standard, it can be
considered that the training is completed.
[0056] Through analysis of the target spectrogram by the convolutional
neural network
model that has completed training, a man machine classification result of the
target spectrogram
can be obtained, i.e., the target spectrogram is of machine or non-machine.
[0057] The man machine classification of the target spectrogram of a voice
to be verified by
a convolutional neural network model is equivalent to the secondary
verification of the voice to
be verified and is mainly used to recognize machine synthesized voices.
Therefore, when the
man machine classification result of the target spectrogram is of non-machine,
it can be
considered that the voice to be verified is a real human voice, thereby
finally judging that the
voice to be verified has passed the verification.
[0058] In an exemplary embodiment, the man machine classification of a
target spectrogram
can be achieved through a machine judgment probability outputted by a
convolutional neural
network model. When a convolutional neural network model analyzes a target
spectrogram, the
convolutional neural network model can output a machine judgment probability
based on a level
of similarity between the target spectrogram and a characteristic spectrogram
of machine
synthesized voices. The higher the probability is, the more likely the target
spectrogram is of
machine. If the machine judgment probability is greater than a probability
threshold, it can be
considered that the man machine classification result of the target
spectrogram is a machine. The
probability threshold can be set and adjusted according to training and actual
application
condition to accurately differentiate target spectrograms of machine and non-
machine.
[0059] In an exemplary embodiment, obtaining a verification code at step
S210 may
comprise: obtaining a verification code that has undergone disturbance
processing.
[0060] Here, disturbance processing can be addition of information that
disturbs recognition
of the verification code, thereby raising difficulty of machine recognition.
Example: if the
verification code is a text verification code, disturbance processing can be
addition of a
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disturbing character in the text verification code; if the verification code
is a voice verification
code, disturbance processing can be addition of disturbing music and sound
waves at a specific
frequency to the voice verification code; if the verification code is an image
verification code,
disturbance processing can be change of image color of the image verification
code, reduction of
image resolution and disorganization of image composition sequence of the
image verification
code; if the verification code is a text message verification code,
disturbance processing can be
addition of out-of-order letters or numerical strings.
[0061] In an exemplary embodiment, obtaining a verification code added with
disturbance
information may comprise the following steps:
[0062] obtaining a preset text and converting the preset text into a target
picture; and
[0063] processing the target picture by one or more of deformation,
discoloring,
fuzzification and increase of noisy points to generate an image verification
code that has
undergone disturbance processing.
[0064] FIG. 4 a flow chart of obtaining a verification code as described
above. As shown in
FIG. 4, firstly, a preset text is obtained. The preset text can be obtained
from a text library and
may also be a generated random number, etc. The obtained preset text is
converted into a target
picture. The target picture can be a picture containing text information of a
verification code,
such as a picture containing characters of a text verification code, or a
picture of image
verification code fragments. During conversion of a target picture, wordart of
the verification
code can be generated and inlaid in a specific background to obtain the target
picture;
alternatively, as shown in FIG. 4, characters of the verification code are
split, each character
generates a word picture, the sequence is disorganized, and a target picture
is spliced. The
generated target picture can be tailored according to the needs and then go
through disturbance
processing. The deformation can be stretching, rotation, liquefaction and
other processing of the
picture; the discoloring refers to changing color gradation parameters of the
target picture, for
example, converting the target picture into a grey-scale map and adjusting
brightness distribution;
the fuzzification refers to reducing the resolution of the target picture, for
example, conducting
local pixel compression of the target picture; and the increase of noisy
points can be addition of
grains that affect recognition to the target picture. In addition, disturbing
elements can be
randomly added to the target picture, too, for example, horizontal lines or
vertical lines are added
to the target picture. Those skilled in the art can readily think of that
other disturbance processing
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methods also should be included in the scope of protection of the present
disclosure.
[0065] In an exemplary embodiment, before response to match between the
voice to be
verified and the verification code, the voice information verification method
may further
comprise the following steps:
[0066] detecting a length of the voice to be verified.
[0067] judging the voice to be verified is failed in the verification and
returning a failure
prompt message to the user terminal if the length of the voice to be verified
is smaller than a
preset length; and
[0068] converting the voice to be verified into a text to be verified and
matching the text to
be verified with the verification code if the length of the voice to be
verified is greater than or
equal to a preset length.
[0069] Here, the length of the voice to be verified can be a duration or a
file size of the voice
to be verified. Considering that an excessively short voice to be verified
might be caused by a
wrong input, leading to meaningless verification and affecting work efficiency
of the server, a
preset length can be set as a criterion for length of a voice to be verified
to filter out the
foregoing condition. For example, the preset length is set to be 2s, voices of
less than 2s will be
automatically filtered out by the server and a failure prompt message will be
returned; for
another example, the preset length is set to be 5KB, then voices to be
verified to be less than
5KB will be automatically filtered out by the server.
[0070] After it is detected that the length of a voice to be verified is
greater than or equal to
the preset length, the voice to be verified can be converted into a text to be
verified. Here, the
text to be verified is information to be verified in a text form corresponding
to the content of the
voice to be verified, and can achieve conversion by a voice text conversion
tool.
[0071] The foregoing preset length can be considered as a lower limit of
length of a voice to
be verified. It needs to be supplemented that there might be sound recording
files wrongly input
or wrongly sent by the user, resulting in excessively complex or lengthy
voices to be verified
received by the server. This may cause processing difficulty of the server and
add meaningless
work. Therefore, an upper limit of the length of a voice to be verified can be
set, too to filter out
the foregoing excessively complex or lengthy voices to be verified. When the
length of a voice
exceeds the upper limit of voice length, a result of verification failure can
be output, and a failure
prompt message is returned. Further, an upper limit of recording time of a
voice to be verified
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can be set in a client program. When the upper limit is exceeded, voice input
is terminated
automatically, and the input voice is sent to the server to conduct voice
information verification.
[0072] Further, a voice to be verified can be converted into a text to be
verified through the
following steps:
[0073] pre-processing the voice to be verified by one or more of sound
track conversion,
pre-emphasis, speech enhancement and blank deletion; and
[0074] converting the pre-processed voice to be verified into the text to
be verified by a time
delay neural network model.
[0075] Considering that the voice to be verified obtained by the server
might contain other
noises and influencing factors, posing a barrier to the processing and
recognition of the voice to
be verified, the voice to be verified can be preprocessed when the voice to be
verified is
converted into a text to be verified. Here, the sound track conversion means
that if voice
characteristics of a voice to be verified that are to be extracted do not
differentiate sound tracks, a
multi-track voice to be verified can be converted into single track; the pre-
emphasis can reserve
voice signals of a voice to be verified within a specific frequency range,
facilitating server's
analysis of information of the voice to be verified; the speech enhancement
can filter out noise
from a voice to be verified to extract pure voice signals; the blank deletion
refers to removing
fragments without actual signals from a voice to be verified. For example,
when a user is
interrupted or is thinking during input of a voice to be verified, the voice
to be verified might
have noise or other blank and invalid voice fragments. The blank deletion can
also reduce
duration and file size of the voice to be verified and lower processing volume
of the server.
When outside noise is too large, for example a user is in a heavy human
traffic or is affected by
other sound devices, before a voice to be verified is processed, voice
activity detection can be
resorted to judge if the voice to be verified has any voice information that
can be verified. If not,
a result of verification failure can be output. Further, those skilled in the
art can readily think of
that other preprocessing methods also should be included in the scope of
protection of the
present disclosure.
[0076] After preprocessing, a voice to be verified can be input to a time
delay neural
network model. The time delay neural network model can frame and recognize the
voice to be
verified and eventually convert the voice to be verified into a corresponding
text to be verified.
[0077] In an exemplary embodiment, the foregoing verification code may
comprise a text
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verification code and matching the text to be verified with the verification
code may comprise
the following steps:
[0078] matching the text to be verified with the text verification code to
obtain a typo
proportion of the text to be verified.
[0079] Accordingly, step S220 may comprise the following steps:
[0080] judging whether the voice to be verified is of non-machine or not if
the typo
proportion is lower than a threshold of matching.
[0081] Here, the typo proportion can be a percentage of the number of
characters that are not
matched successfully to the total number of characters. There may be a
plurality of matching
methods, such as forward matching, reverse matching or two-way matching. The
matching
results likely vary with the selected matching methods. For example, if a text
verification code is
"it is a lovely day today", a text to be verified is "the gas is lovely today"
and forward matching
is adopted, the typo proportion of the matching result will be higher than the
typo proportion
from two-way matching. Under normal conditions, two-way matching can raise
accuracy of
matching calculation of a server, but also adds on computing tasks of the
server and has higher
requirements for configuration of the server. Forward matching and reverse
matching have lower
requirements for computing power of the server, and a smaller processing
volume. An
appropriate matching method can be selected according to the actual condition.
[0082] The threshold of matching can be a set acceptable upper limit of
typo proportion of
matching. Considering that there might be an error when a server converts a
voice to be verified
into a text to be verified and matches a text to be verified with a text
verification code, certain
inconsistency between a text to be verified and a text verification code is
allowed. A threshold of
matching can be set according to the actual condition. For example, when a
verification code is
shorter or there is less disturbing information, a higher threshold of
matching can be set; when a
verification code is longer or the content is more complex, a lower threshold
of matching can be
set. There is not any specific limitation to a threshold of matching in this
embodiment. When the
typo proportion is lower than the threshold of matching, it can be considered
that the text to be
verified is successfully matched with the verification code, i.e., the voice
to be verified is
successfully matched with the verification code and passes the primary
verification, and the
secondary verification can be started, i.e., man machine judgment is conducted
on the voice to be
verified.
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[0083] FIG. 5 shows a sub-flow chart of a voice information verification
method in this
exemplary embodiment. As shown in FIG. 5, after a voice to be verified is
obtained, whether the
length of the obtained voice to be verified reaches the preset length or not
can be judged at first.
If the length of the obtained voice to be verified is less than the preset
length, a result of
verification failure can be output directly; if the length of the obtained
voice to be verified is up
to the preset length, the voice to be verified can be preprocessed, and
converted by a time delay
neural network model into a text to be verified. Then the text to be verified
is matched with a
verification code, and whether the typo proportion is lower than the threshold
of matching or not
is judged. If the typo proportion is not lower than the threshold of matching,
a result of
verification failure can be output; if the typo proportion is lower than the
threshold of matching,
the voice to be verified can be converted into a target spectrogram, for
subsequent verification.
[0084] In an exemplary embodiment, converting a voice to be verified into a
target
spectrogram may comprise: converting the voice to be verified into a target
spectrogram by the
short-time Fourier transform.
[0085] The short-time Fourier transform can convert complex sound signals
to a frequency
domain, and then analyze time domain signal characteristics of a voice to be
verified according
to spectral characteristics. For example, a plurality of instantaneous
fragments can be extracted
from the voice to be verified, and arranged according to a time sequence. Each
fragment is
converted into a frequency ¨ energy image, thereby obtaining an arrangement
sequence of a
plurality of target spectrograms, which can be processed using a convolutional
neural network
model subsequently.
[0086] FIG. 6 shows a flow chart of a voice information verification method
in this
exemplary embodiment. As shown in FIG. 6, a server can obtain a preset text
from a text library
and obtain a verification code through disturbance processing. A user terminal
obtains a voice to
be verified input by a user, preprocesses the voice to be verified, then
converts the voice to be
verified into a text to be verified by a time delay neural network model, and
calculates a typo
proportion to match the text to be verified and the verification code. If the
matching is successful,
the voice to be verified can be converted into a target spectrogram by the
short-time Fourier
transform. Further, the target spectrogram is analyzed by a convolutional
neural network model
that has been trained and tested to obtain a man machine classification
result. If the man machine
classification result is of non-machine, a result of final successful
verification is output.
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[0087] Exemplary embodiments of the present disclosure further provide a
voice
information verification apparatus. As shown in FIG. 7, the apparatus 700 may
comprise: an
information obtaining module 710, for obtaining a verification code and a
voice to be verified
sent by a user terminal regarding the verification code; a man machine
judgment module 720, for
judging whether the voice to be verified is of non-machine or not in response
to match between
the voice to be verified and the verification code; and a voice verification
module 730, for
determining that the voice to be verified has passed the verification if the
voice to be verified is
of non-machine.
[0088] In an exemplary embodiment, the man machine judgment module may
comprise: a
spectrogram conversion unit, for converting a voice to be verified into a
target spectrogram if the
voice to be verified is matched with a verification code; a spectrogram
analysis unit, for
analyzing the target spectrogram by a convolutional neural network model to
obtain a man
machine classification result of the target spectrogram; and a voice judgment
unit, for
determining whether the voice to be verified is of non-machine or not based on
the man machine
classification result.
[0089] In an exemplary embodiment, the information obtaining module may be
further used
for obtaining a verification code that has undergone disturbance processing.
[0090] In an exemplary embodiment, the information obtaining module may
comprise: a
text obtaining unit, for obtaining a preset text and converting the preset
text into a target picture;
a picture processing unit, for processing the target picture by one or more of
deformation,
discoloring, fuzzification and increase of noisy points to generate an image
verification code that
has undergone disturbance processing.
[0091] In an exemplary embodiment, the voice information verification
apparatus may
further comprise: a voice length detection unit, for detecting the length of a
voice to be verified;
a preset length judgment unit, for judging the voice to be verified is failed
in the verification and
returning a failure prompt message to the user terminal if the length of the
voice to be verified is
smaller than a preset length, and for converting the voice to be verified into
a text to be verified
and matching the text to be verified with the verification code if the length
of the voice to be
verified is greater than a preset length.
[0092] In an exemplary embodiment, the preset length judgment unit may
further comprise:
a preprocessing subunit, for pre-processing the voice to be verified by one or
more of sound
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track conversion, pre-emphasis, speech enhancement and blank deletion; and a
model processing
unit, for converting the pre-processed voice to be verified into the text to
be verified by a time
delay neural network model.
[0093] In an exemplary embodiment, the verification code may further
comprise a text
verification code, and the preset length judgment unit may further comprise: a
text matching unit,
for matching the text to be verified with the text verification code to obtain
a typo proportion of
the text to be verified; and a man machine judgment module, which can be used
to judge whether
the voice to be verified is of non-machine or not if the typo proportion is
lower than a threshold
of matching.
[0094] Details of the foregoing modules / units have been elaborated in
embodiments of the
corresponding method, so they are not described here again.
[0095] In an exemplary embodiment of the present disclosure, an electronic
device that can
achieve the foregoing method is further provided.
[0096] Those skilled in the art should understand that all aspects of the
present disclosure
can be achieved as system, method or program products, so all aspects of the
present disclosure
can be specifically achieved in the following forms: an implementation manner
of complete
hardware, an implementation manner of complete software (including firmware,
microcode, etc.),
or an implementation manner of combination of hardware and software. They are
collectively
referred to as "circuits", "modules" or "systems" here.
[0097] Below an electronic device 800 according to this exemplary
embodiment of the
present disclosure is described with reference to FIG. 8. The electronic
device 800 shown in FIG.
8 is only an example and should not cause any limitation to the functions and
use of
embodiments of the present invention.
[0098] As shown in FIG. 8, the electronic device 800 is manifested in form
of a
general-purpose computing device. Components of the electronic device 800 may
include
without limitation: the foregoing at least one processing unit 810, the
foregoing at least one
memory unit 820, a bus 830 connecting various system components (including the
memory unit
820 and the processing unit 810) and a display unit 840.
[0099] The memory unit stores program codes, which can be executed by the
processing
unit 810 so that the processing unit 810 implements the steps according to
exemplary
implementation manners of the present invention as described in the foregoing
"exemplary
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method" section of the Description. For example, the processing unit 810 can
implement steps
S210¨S250 as shown in FIG. 1:
[0100] The memory unit 820 may comprise a readable medium in form of a
volatile memory
unit, such as random access memory unit (RAM) 821 and / or cache memory unit
822, and may
further comprise a read only memory unit (ROM) 823.
[0101] The memory unit 820 may further comprise a program / utility tool
824 comprising a
group of (at least one) program modules 825. Such program module 825 includes
without
limitation: operating system, one or more application programs, other program
modules and
program data. One or a combination of these examples may include realization
of a network
environment.
[0102] The bus 830 can be a local bus expressing one or more of a few kinds
of bus
structures, including memory unit bus or memory unit controller, peripheral
bus, accelerated
graphic port and processing unit, or using any of the plurality of bus
structures.
[0103] Alternatively, the electronic device 800 can also communicate with
one or more
peripherals 1000 (such as keyboard, pointing device, and Bluetooth device),
and can also
communicate with one or more communication devices that enable a user to
interact with the
electronic device 800, and/or communicate with any device (such as router and
modem) that
enables the electronic device 800 to communicate with one or more other
computing devices.
Such communication can proceed via an I/0 interface 850. Further, the
electronic device 800
may also communicate with one or more networks (such as local area network
(LAN), wide area
network (WAN) and/or public network, such as Internet) via a network adapter
860. As shown in
the figure, the network adapter 860 communicates with other modules of the
electronic device
800 via the bus 830. It should be understood that though not shown in the
figure, other hardware
and/or software modules can be used in combination with the electronic device
800, including
but not limited to: microcode, device driver, redundant processing unit,
peripheral disk drive
array, RAID system, tape driver and data backup storage system.
[0104] From description of the foregoing implementation manners, those
skilled in the art
can easily understand that the implementation manners described here can be
achieved by means
of software or by means of software combined with necessary hardware.
Therefore, a technical
solution according to an implementation manner of the present disclosure can
be embodied in
form of a software product. The software product can be stored in a non-
volatile memory
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medium (it can be a CD-ROM, a USB disk, or a mobile hard disk, or others) or
in a network and
comprises a number of instructions so that a computing device (it can be a
personal computer, a
server, a network device, or others) implements the foregoing method according
to an exemplary
embodiment of the present disclosure.
[0105] In the exemplary embodiments of the present disclosure, a computer
readable
memory medium is further provided and stores program products that can achieve
the foregoing
method of the Description. In some possible implementation manners, all
aspects of the present
invention can also be achieved as a form of program product, including program
codes. When
the program product runs on a terminal device, the program codes are used to
make the terminal
device implement the steps according to exemplary implementation manners of
the present
invention as described in the foregoing "exemplary method" section of the
Description.
[0106] As shown in FIG. 9, a program product 900 used to achieve the
foregoing method
according to exemplary embodiments of the present invention is described. The
program product
900 may adopt portable compact disk read only memory (CD-ROM) and include
program codes
and can run on a terminal device, such as a personal computer. However, the
program product of
the present invention is not limited to this. In this document, a readable
memory medium can be
any tangible medium containing or storing programs, which can be used by an
instruction
execution system, apparatus or device or can be used in combination with an
instruction
execution system, apparatus or device.
[0107] The program product can adopt any combination of one or more
readable media. The
readable media can be readable signal media or readable memory media. The
readable memory
media for example can be, without limitation, electric, magnetic, optical,
electromagnetic,
infrared or semiconductor systems, apparatuses or devices, or any combination
thereof More
concrete examples of the readable memory media (an inexhaustive list) include:
electric
connection with one or more conductors, portable disk, hard disk, random
access memory
(RAM), read only memory (ROM), erasable programmable read-only memory (EPROM
or flash
memory), optical fiber, portable compact disk read only memory (CD-ROM),
optical memory
module, magnetic memory module or any appropriate combination thereof
[0108] Computer readable signal media may include data signals transmitted
in base band or
as part of carriers, and carry readable program codes. Such transmitted data
signals can adopt
various forms, including but not limited to: electromagnetic signals, optical
signals or any
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appropriate combination thereof. The readable signal media may also be any
readable media
except readable memory media. Such readable media can send, transmit or
transfer programs
used by an instruction execution system, apparatus or device or used in
combination with an
instruction execution system, apparatus or device.
[0109] The program codes contained on a readable medium can be transferred
by any
medium, including but not limited to: wireless, wired, optical cable and RF,
or any appropriate
combination the foregoing thereof.
[0110] Any combination of one or more programming languages can be used to
write
program codes used to implement operations of the present disclosure. The
programming
languages include object-oriented programming languages ¨ such as Java and
C++, and further
include conventional procedural programming languages ¨ such as "C" language
or similar
programming languages. Program codes can be totally executed on a user's
computing device, or
partially executed on a user's device, or executed as independent software
package, or partially
executed on a user's computing device and partially executed on a remote
computing device, or
totally executed on a remote computing device or a server. Under a
circumstance involving a
remote computing device, the remote computing device can be connected to the
user's
computing device via any type of network, including local area network (LAN)
or wide area
network (WAN), or can be connected to an external computing device (e.g.,
connected via the
Internet through an Internet service provider).
[0111] Further, the foregoing accompanying drawings are only schematic
description of
processing included according to the method of exemplary embodiments of the
present
disclosure, and not intended to set any limitation. It can be easily
understood that processing
shown in the foregoing accompanying drawings does not indicate or limit a time
order of the
processing. Further, it can be easily understood, too that the processing may
be executed
synchronously or asynchronously in a plurality of modules for example.
[0112] It should be noted that the detailed description above has mentioned
several modules
or units of the device used to implement actions, but such division is not
compulsory. In fact,
according to the exemplary embodiments of the present disclosure, the
characteristics and
functions of two or more modules or units described above can be embodied in
one module or
unit. Conversely, the characteristics and functions of one module or unit
described above can be
further divided into embodiment by a plurality of modules or units.
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[0113] After considering the Description and practicing the invention
disclosed here, those
skilled in the art can easily think of other embodiments of the present
disclosure. The present
disclosure is intended to cover any modification, use or adaptive change of
the present disclosure.
These modifications, uses or adaptive changes follow the general principle of
the present
disclosure and include the common general knowledge or conventional technical
means in the
technical field not disclosed by the present disclosure. The Description and
embodiments are
exemplary only. The real scope and spirit of the present disclosure are stated
by the following
Claims.
[0114] It should be understood that the present disclosure is not limited
to the accurate
structure described above and shown in the accompanying drawings and may have
various
modifications and changes without departing from its scope. The scope of the
present disclosure
is limited only by the attached Claims.
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