System and method for data augmentation and speech processing in dynamic acoustic environments

1. A computer-implemented method for data augmentation, executed on a computing device, comprising:

defining a model representative of a plurality of acoustic variations to a speech signal associated with an adaptive beamforming, thus defining a plurality of time-varying spectral modifications, wherein the plurality of acoustic variations to the speech signal include frequency-based variations in a speech signal beampattern from a movement of a plurality of beampatterns formed by a microphone array configured for the adaptive beamforming and a beamsteering by dynamically modifying and steering the plurality of beampatterns toward a speaker; and

applying the plurality of time-varying spectral modifications to a plurality of feature coefficients of a target domain of a reference signal using a filtering operation, thus generating a plurality of time-varying spectrally-augmented feature coefficients of the reference signal,

wherein applying the plurality of time-varying spectral modifications to the plurality of feature coefficients of the target domain of the reference signal includes:

generating, via a machine learning model, a mapping of the plurality of acoustic variations to one or more feature coefficients of the target domain representative of the frequency-based variations in the speech signal beampattern from the model representative of the plurality of acoustic variations,

applying, via the machine learning model, the mapping of the plurality of acoustic variations to the plurality of feature coefficients of the reference signal, and

generating, via the machine learning model, augmented data from the reference signal and one or more parameters associated with a particular acoustic variation of the plurality of acoustic variations.

2. The computer-implemented method of claim 1 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes one or more of:

modeling the plurality of acoustic variations to the speech signal as a statistical distribution; and

modeling the plurality of acoustic variations to the speech signal as a mathematical model representative of the plurality of acoustic variations associated with a particular use-case scenario.

3. The computer-implemented method of claim 1 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes receiving one or more inputs associated with the one or more of a speaker location and a speaker orientation relative to a microphone.

4. The computer-implemented method of claim 1 , wherein applying the plurality of time-varying spectral modifications to the reference signal using the filtering operation includes one or more of:

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying parameters in time domain filtering; and

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying multiplication factors in frequency domain filtering.

5. The computer-implemented method of claim 3 , further comprising:

training a speech processing system using the time-varying spectrally-augmented signal and the one or more inputs associated with one or more of the speaker location and the speaker orientation relative to the microphone.

6. The computer-implemented method of claim 1 , further comprising:

training a speech processing system using the time-varying spectrally-augmented signal, thus defining a trained speech processing system.

7. The computer-implemented method of claim 6 , further comprising:

performing speech processing via the trained speech processing system, wherein the trained speech processing system is executed on at least one computing device.

8. A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

defining a model representative of a plurality of acoustic variations to a speech signal associated with an adaptive beamforming, thus defining a plurality of time-varying spectral modifications, wherein the plurality of acoustic variations to the speech signal include frequency-based variations in a speech signal beampattern from a movement of a plurality of beampatterns formed by a microphone array configured for the adaptive beamforming and a beamsteering by dynamically modifying and steering the plurality of beampatterns toward a speaker; and

applying the plurality of time-varying spectral modifications to a plurality of feature coefficients of a target domain of a reference signal using a filtering operation, thus generating a plurality of time-varying spectrally-augmented feature coefficients of the reference signal,

wherein applying the plurality of time-varying spectral modifications to the plurality of feature coefficients of the target domain of the reference signal includes:

generating, via a machine learning model, a mapping of the plurality of acoustic variations to one or more feature coefficients of the target domain representative of the frequency-based variations in the speech signal beampattern from the model representative of the plurality of acoustic variations,

applying, via the machine learning model, the mapping of the plurality of acoustic variations to the plurality of feature coefficients of the reference signal, and

generating, via the machine learning model, augmented data from the reference signal and one or more parameters associated with a particular acoustic variation of the plurality of acoustic variations.

9. The computer program product of claim 8 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes one or more of:

modeling the plurality of acoustic variations to the speech signal as a statistical distribution; and

modeling the plurality of acoustic variations to the speech signal as a mathematical model representative of the plurality of acoustic variations associated with a particular use-case scenario.

10. The computer program product of claim 8 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes receiving one or more inputs associated with the one or more of a speaker location and a speaker orientation relative to a microphone.

11. The computer program product of claim 8 , wherein applying the plurality of time-varying spectral modifications to the reference signal using the filtering operation includes one or more of:

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying parameters in time domain filtering; and

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying multiplication factors in frequency domain filtering.

12. The computer program product of claim 10 , further comprising:

training a speech processing system using the time-varying spectrally-augmented signal and the one or more inputs associated with one or more of the speaker location and the speaker orientation relative to the microphone.

13. The computer program product of claim 8 , further comprising:

training a speech processing system using the time-varying spectrally-augmented signal, thus defining a trained speech processing system.

14. The computer program product of claim 13 , further comprising:

performing speech processing via the trained speech processing system, wherein the trained speech processing system is executed on at least one computing device.

15. A computing system comprising:

a memory; and

a processor configured to define a model representative of a plurality of acoustic variations to a speech signal associated with an adaptive beamforming, thus defining a plurality of time-varying spectral modifications, wherein the plurality of acoustic variations to the speech signal include frequency-based variations in a speech signal beampattern from a movement of a plurality of beampatterns formed by a microphone array configured for the adaptive beamforming and a beamsteering by dynamically modifying and steering the plurality of beampatterns toward a speaker, wherein the processor is further configured to apply the plurality of time-varying spectral modifications to a plurality of feature coefficients of a target domain of a reference signal using a filtering operation, thus generating a plurality of time-varying spectrally-augmented feature coefficients of the reference signal, wherein applying the plurality of time-varying spectral modifications to the plurality of feature coefficients of the target domain of the reference signal includes: generating, via a machine learning model, a mapping of the plurality of acoustic variations to one or more feature coefficients of the target domain representative of the frequency-based variations in the speech signal beampattern from the model representative of the plurality of acoustic variations, applying, via the machine learning model, the plurality of time-varying spectral modifications to the plurality of feature coefficients of the reference signal, and generating, via the machine learning model, augmented data from the reference signal and one or more parameters associated with a particular acoustic variation of the plurality of acoustic variations.

16. The computing system of claim 15 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes one or more of:

modeling the plurality of acoustic variations to the speech signal as a statistical distribution; and

modeling the plurality of acoustic variations to the speech signal as a mathematical model representative of the plurality of acoustic variations associated with a particular use-case scenario.

17. The computing system of claim 15 , wherein defining the model representative of the plurality of acoustic variations to the speech signal includes receiving one or more inputs associated with the one or more of a speaker location and a speaker orientation relative to a microphone.

18. The computing system of claim 15 , wherein applying the plurality of time-varying spectral modifications to the reference signal using the filtering operation includes one or more of:

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying parameters in time domain filtering; and

applying the plurality of time-varying spectral modifications to the reference signal using a plurality of time-varying multiplication factors in frequency domain filtering.