CN107919136B - Digital voice sampling frequency estimation method based on Gaussian mixture model - Google Patents
Digital voice sampling frequency estimation method based on Gaussian mixture model Download PDFInfo
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- 238000005070 sampling Methods 0.000 title claims abstract description 56
- 239000000203 mixture Substances 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 13
- 239000013598 vector Substances 0.000 claims description 7
- 238000009432 framing Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 2
- 238000001228 spectrum Methods 0.000 description 2
- 241000287196 Asthenes Species 0.000 description 1
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- 238000006731 degradation reaction Methods 0.000 description 1
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
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Abstract
The invention discloses a Gaussian mixture model-based digital voice sampling frequency estimation method, which comprises the steps of firstly, generating a GMM by using high-sampling-rate digital voice training; then interpolating the input voice with low sampling rate to be estimated, and increasing the sampling frequency; and finally, carrying out probability calculation on the interpolated digital voice by using the GMM, and adjusting the interpolation multiple according to the calculation result to enable the output probability of the GMM to reach the maximum value, thereby obtaining the sampling frequency of the input voice. The invention can identify the sampling frequency of unknown digital voice and reduce the system performance reduction caused by the mismatching of the sampling frequency.
Description
Technical Field
The invention belongs to the field of voice processing, and particularly relates to a voice processing method for estimating the sampling frequency of input voice by using a Gaussian mixture model generated by high-sampling-rate digital voice training.
Background
The voice is a basic means for human to communicate information and is also the most convenient and effective human-computer interaction tool in the motion process. Digital speech has the advantages of high accuracy and easy storage and transmission, but different digital systems have different computational performance, access speed, storage space, battery capacity and application, and thus different sampling frequencies are used. If the sampling frequency of the input speech does not match the sampling frequency of the digital system, this can lead to a degradation of the performance of the speech processing system. Therefore, it is necessary to transform the input speech to match the sampling frequency with the digital system, thereby enhancing the practical application capability of the speech processing system.
If the sampling frequency of the input voice is known, only the ratio of the sampling frequency to the system sampling frequency needs to be calculated, and then interpolation or decimation is carried out on the input voice to enable the sampling frequency to be consistent with the system. However, in some applications, the sampling frequency of the input speech is unknown. For example, the audio on the network is monitored, capturing a digital speech signal segment whose sampling frequency may be unknown.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a digital speech sampling frequency estimation method based on a Gaussian Mixture Model (GMM). In the method, firstly, a GMM is generated by high sampling rate digital voice training; then interpolating the input voice with low sampling rate to be estimated, and increasing the sampling frequency; and finally, carrying out probability calculation on the interpolated digital voice by using the GMM, and adjusting the interpolation multiple according to the calculation result to enable the output probability of the GMM to reach the maximum value, thereby obtaining the sampling frequency of the input voice.
The method comprises the following specific steps:
(1) sampling training voice at 48kHz, windowing and framing the training voice, extracting cepstrum characteristics, and training by using the characteristic vectors of all voice units to generate a Gaussian mixture model;
(2) interpolating low-sampling-rate input voice to be estimated (namely voice with the sampling frequency lower than 48 kHz) to improve the sampling frequency;
(3) inputting the interpolated digital voice into GMM, and calculating the output probability;
(4) repeating (2) and (3) for all interpolation multiples, and recording the output probability of each time;
(5) and comparing the output probabilities corresponding to all the interpolation multiples, wherein the interpolation multiple corresponding to the maximum output probability is the ratio of the training voice sampling frequency to the input voice sampling frequency.
Drawings
Fig. 1 is an overall framework of a digital speech sampling frequency estimation system based on a gaussian mixture model, which mainly comprises a model training module, a signal interpolation module, an interpolation multiple control module and a frequency estimation module.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, the method for estimating the frequency of digital speech samples based on the gaussian mixture model mainly includes model training, signal interpolation, interpolation multiple control and frequency estimation modules. The specific embodiments of the various main modules in the drawings are described in detail below:
1. model training
Firstly, sampling training voice at 48kHz, windowing, framing, and carrying out fast Fourier transform on each frame of voice signal to obtain an amplitude spectrum of each frame of signal; then, performing Mel filtering on the magnitude spectrum of each frame of signal, and taking the logarithm to obtain the cepstrum characteristic parameter of the training voice; and finally, training by using the feature vectors of all the phonetic units to generate a Gaussian mixture model:
wherein o istA cepstrum feature vector representing the t frame of training speech; c. Cm、μmSum-sigmamRespectively representing the mixing coefficient, the mean vector and the covariance matrix of the mth Gaussian unit in the GMM.
2. Signal interpolation
Because the gaussian mixture model is trained by training speech with a high sampling rate, the sampling frequency of the input digital speech can be considered to be lower than that of the training speech, and the input speech is interpolated to match the sampling frequency with the GMM.
Let the interpolation multiple be DiThen, the input digital voice x (n) is interpolated to obtain xi(n)：
Interpolated digital speech xi(n) the sampling frequency is D of the original input digital speech x (n)iAnd (4) doubling.
3. Interpolation multiple control
Using the sampling frequency of training speech and the sampling frequency f of a group of common speech1，f2，…，fi，…，fNRatio D of1，D2，…，Di，…，DNAs the initial value interpolation multiple.
4. Frequency estimation
And inputting the digital voice after each interpolation into a Gaussian mixture model, and calculating the output probability of the digital voice. Comparing the output probabilities corresponding to all the interpolation multiples to determine the interpolation multiple that maximizes the output probability
And is arranged at
And (4) fine-tuning the interpolation multiple nearby to enable the output probability of the GMM to reach the maximum value. If the interpolation multiple at this time is recorded as
Then the sampling frequency of the original input speech
It can be estimated that:
wherein f is0The sampling frequency for the high rate training speech is here taken to be 48 kHz.
After the sampling frequency of the input voice is estimated, the input voice can be interpolated according to the sampling frequency of the target system, and then the input voice is input into the target system for processing.
Claims (5)
1. A digital voice sampling frequency estimation method based on a Gaussian mixture model is characterized in that firstly, a GMM is generated by high sampling rate digital voice training; then interpolating the input voice with low sampling rate to be estimated, and increasing the sampling frequency; and finally, carrying out probability calculation on the interpolated digital voice by using the GMM, and adjusting the interpolation multiple according to the calculation result to enable the output probability of the GMM to reach the maximum value, thereby obtaining the sampling frequency of the input voice.
2. The method for estimating the sampling frequency of the digital voice based on the Gaussian mixture model as claimed in claim 1, specifically comprising:
(1) sampling training voice at 48kHz, windowing and framing the training voice, extracting cepstrum characteristics, and training by using the characteristic vectors of all voice units to generate a Gaussian mixture model;
(2) interpolating the input voice with low sampling rate to be estimated, and increasing the sampling frequency;
(3) inputting the interpolated digital voice into GMM, and calculating the output probability;
(4) repeating (2) and (3) for all interpolation multiples, and recording the output probability of each time;
(5) and comparing the output probabilities corresponding to all the interpolation multiples, wherein the interpolation multiple corresponding to the maximum output probability is the ratio of the training voice sampling frequency to the input voice sampling frequency.
3. The method for estimating the sampling frequency of the digital speech based on the Gaussian mixture model as claimed in claim 2, wherein the Gaussian mixture model generated in the step (1) is:
wherein o istA cepstrum feature vector representing the t frame of training speech; c. Cm、μmSum-sigmamRespectively representing the mixing coefficient, the mean vector and the covariance matrix of the mth Gaussian unit in the GMM.
4. The method of claim 2, wherein the interpolation is performed on the low-sampling-rate input speech to be estimated:
let the interpolation multiple be DiThen, the input digital voice x (n) is interpolated to obtain xi(n)：
Interpolated digital speech xi(n) the sampling frequency is D of the original input digital speech x (n)iAnd (4) doubling.
5. The method of claim 4, wherein the sampling frequency of the training speech and the sampling frequency f of the set of common speech are used1，f2，…，fi，…，fNRatio D of1，D2，…，Di，…，DNAs the initial value interpolation multiple.
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Citations (2)
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| US8639502B1 (en) * | 2009-02-16 | 2014-01-28 | Arrowhead Center, Inc. | Speaker model-based speech enhancement system |
| CN107204840A (en) * | 2017-07-31 | 2017-09-26 | 电子科技大学 | Sinusoidal signal frequency method of estimation based on DFT and iteration correction |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8639502B1 (en) * | 2009-02-16 | 2014-01-28 | Arrowhead Center, Inc. | Speaker model-based speech enhancement system |
| CN107204840A (en) * | 2017-07-31 | 2017-09-26 | 电子科技大学 | Sinusoidal signal frequency method of estimation based on DFT and iteration correction |
Non-Patent Citations (3)
| Title |
|---|
| 《Improved AdaBoost Algorithm Using VQMAP for Speaker Identification》;Haiyang Wu et al.;《2010 International Conference on Electrical and Control Engineering》;20101231;全文 * |
| 《Smooth interpolation of Gaussian mixture models》;Petr Zelinka et al.;《2009 19th International Conference Radioelektronika》;20091231;全文 * |
| 《VTS feature compensation based on two-layer GMM structure for robust speech recognition》;Lin Zhou et al.;《 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)》;20161231;全文 * |
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