# US 10,319,377 B2 · Method and system of estimating clean speech parameters from noisy speech parameters

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,319,377 B2
- Document ID: US10319377B2
- Status: Granted
- Kind: B2
- Application number: 15/444,759
- Filed: 2017-02-28
- Published: 2017-09-21
- Granted: 2019-06-11
- Earliest claimed: 2016-03-15
- Assignees: Tata Consultancy Services Limited
- Applicants: Tata Consultancy Services Limited
- Inventors: Ashish Panda; Sunil Kumar Kopparapu
- Web page: https://patentgrep.com/patent/US10319377B2

## CPC

- G10L 15/20: Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech (G10L21/02 takes precedence)
- G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit
- G10L 17/02: Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
- G10L 17/20: Pattern transformations or operations aimed at increasing system robustness, e.g. against channel noise or different working conditions
- G10L 21/0208: Noise filtering
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A method and system is provided for estimating clean speech parameters from noisy speech parameters. The method is performed by acquiring speech signals, estimating noise from the acquired speech signals, computing speech features from the acquired speech signals, estimating model parameters from the computed speech features and estimating clean parameters from the estimated noise and the estimated model parameters.

## Claims

### Claim 1

A method of estimating clean speech parameters from noisy speech parameters, said method comprising processor implemented steps of:
acquiring speech signals using a speech acquisition module (202);
estimating noise from said acquired speech signals using a noise estimation module (204), wherein estimation of noise is performed through non-speech frames of the acquired speech signals;
computing noisy speech features from said estimated noise using a feature extraction module (206), wherein Mel-Frequency Cepstral Coefficients are used as said noisy speech features;
estimating noisy model parameters from said computed noisy speech features using a parameter estimation module (208); and
estimating clean parameters from said estimated noise and said estimated noisy model parameters using a clean parameter estimation module (210), wherein the estimation is performed using Reverse Psychoacoustic Compensation (RPC),
wherein the RPC is performed iteratively by:
initializing a clean model mean to a certain value of a model mean;
performing Psychoacoustic Compensation on the initialized clean model mean to obtain a compensated clean model mean;
determining if value of the compensated clean model mean is within a certain range of a model mean value;
terminating the iteration in response to determining that the value of the compensated clean model mean is within a certain range of the model mean value; and
updating clean parameters of the clean model mean in response to determining that the value of the compensated clean model mean is outside a certain range of the model mean value.

### Claim 2 (depends on 1)

The method as claimed in claim 1, wherein said speech acquisition module (202) further converts said acquired speech signals from analog to digital waveforms.

### Claim 3 (depends on 1)

The method as claimed in claim 1, wherein said estimation of noise using the noise estimation module is performed during training phase.

### Claim 4 (depends on 1)

The method as claimed in claim 1, wherein said estimated noise and said estimated noisy model parameters are first converted into their spectral domain representations.

### Claim 5 (depends on 1)

The method as claimed in claim 1, wherein the estimated clean parameters are in their spectral domain representation.

### Claim 6 (depends on 5)

The method as claimed in claim 5, wherein the estimated clean parameters are converted from their spectral domain representation to feature domain representation.

### Claim 7

A system of estimating clean speech parameters from noisy speech parameters, said system comprising:
a processor;
a data bus coupled to said processor; and
a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for:
acquiring speech signals;
estimating noise from said acquired speech signals, wherein estimation of noise can be performed through non-speech frames of the acquired speech signals;
computing noisy speech features from said estimated noise, wherein Mel-Frequency Cepstral Coefficients are used as said noisy speech features;
estimating noisy model parameters from said computed noisy speech features;
estimating clean parameters from said estimated noise and said estimated noisy model parameters, wherein the estimation is performed using reverse psychoacoustic compensation (RPC), wherein the RPC is performed iteratively by:
initializing a clean model mean to a certain value of a model mean;
performing Psychoacoustic Compensation on the initialized clean model mean to obtain a compensated clean model mean;
determining if value of the compensated clean model mean is within a certain range of a model mean value;
terminating the iteration in response to determining that the value of the compensated clean model mean is within a certain range of the model mean value; and
updating clean parameters of the clean model mean in response to determining that the value of the compensated clean model mean is outside a certain range of the model mean value.

### Claim 8

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes:
acquiring of speech signals using a speech acquisition module (202);
estimating of noise from said acquired speech signals using a noise estimation module (204), wherein estimation of noise can be performed through non-speech frames of the acquired speech signals;
computing of noisy speech features from said acquired speech signals using a feature extraction module (206), wherein Mel-Frequency Cepstral Coefficients are used as said noisy speech features;
estimating of noisy model parameters from said computed noisy speech features using a parameter estimation module (208);
estimating of clean parameters from said estimated noise and said estimated noisy model parameters using a clean parameter estimation module (210), wherein the estimation is performed using reverse psychoacoustic compensation (RPC), wherein the RPC is performed iteratively by:
initializing a clean model mean to a certain value of a model mean;
performing Psychoacoustic Compensation on the initialized clean model mean to obtain a compensated clean model mean;
determining if value of the compensated clean model mean is within a certain range of a model mean value;
terminating the iteration in response to determining that the value of the compensated clean model mean is within a certain range of the model mean value; and
updating clean parameters of the clean model mean in response to determining that the value of the compensated clean model mean is outside a certain range of the model mean value.

### Claim 9 (depends on 8)

The one or more non-transitory machine readable information storage mediums of claim 8, wherein said speech acquisition module (202) further converts said acquired speech signals from analog to digital waveforms.

### Claim 10 (depends on 8)

The one or more non-transitory machine readable information storage mediums of claim 8, wherein said estimation of noise using the noise estimation module is performed during training phase.

### Claim 11 (depends on 8)

The one or more non-transitory machine readable information storage mediums of claim 8, wherein said estimated noise and said estimated noisy model parameters are first converted into their spectral domain representations.

### Claim 12 (depends on 8)

The one or more non-transitory machine readable information storage mediums of claim 8, wherein the estimated clean parameters are in their spectral domain representation.

### Claim 13 (depends on 12)

The one or more non-transitory machine readable information storage mediums of claim 12, wherein the estimated clean parameters are converted from their spectral domain representation to feature domain representation.