Method and system for speech enhancement

1. A method of training a neural network for de-noising audio enhancement, the method comprising:

creating simulated noisy speech data from high quality speech data; and

performing training on a neural network using the high quality speech data and the simulated noisy speech data to train the neural network to create de-noised speech data given noisy speech data, wherein performing the training includes minimizing errors in the neural network based on a weighted sum of:

a first differentiable loss term based on a decoding error of an Automatic Speech Recognition (ASR) system processing current de-noised speech data results that are generated by the neural network during the training; and

a second differential loss term based on spectral distance between the high quality speech data and the current de-noised speech data results that are generated by the neural network during the training.

2. The method of claim 1 further comprising:

generating the current de-noised speech data results during the training by processing at least a portion of the simulated noisy speech data with the neural network.

3. The method of claim 2 further comprising:

determining the decoding error during the training by comparing (1) speech recognition results generated by the ASR system processing the current de-noised speech data results and (2) a transcript of at least a portion of the high quality speech data upon which the at least a portion of the simulated noisy speech data was created.

4. The method of claim 1 further comprising:

collecting the high quality speech data in a low noise environment.

5. The method of claim 1 further comprising: creating the simulated noisy speech data by adding reverberation to the high quality speech data using convolution.

6. The method of claim 5 further comprising:

adding the reverberation using convolution by accessing a database comprising at least one of: measured impulse responses from a reverberant environment and synthetically generated impulse responses.

7. The method of claim 1 further comprising:

collecting data from an environment in which the ASR system is to be deployed; and

creating the simulated noisy speech data in accordance with the data collected from the environment.

8. The method of claim 1 further comprising:

performing the training by training the neural network to learn a maximum-likely encryption of the high quality speech data given the simulated noisy speech data.

9. The method of claim 1 wherein minimizing the errors in the neural network includes: adjusting one or more weights of the neural network.

10. The method of claim 1 further comprising: after the training, processing noisy speech data using the trained neural network to determine enhanced speech data.

11. The method of claim 1 wherein the training is deep normalizing flow training.

12. A computer system for training a neural network for de-noising audio enhancement, the computer system comprising:

a processor; and

a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions being configured to cause the system to:

create simulated noisy speech data from high quality speech data; and

perform training on a neural network using the high quality speech data and the simulated noisy speech data to train the neural network to create de-noised speech data given noisy speech data, wherein performing the training includes minimizing errors in the neural network based on a weighted sum of:

a first differential loss term based on a decoding error of an Automatic Speech Recognition (ASR) system processing current de-noised speech data results that are generated by the neural network during the training; and

a second differential loss term based on spectral distance between the high quality speech data and the current de-noised speech data results that are generated by the neural network during the training.

13. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

generate the current de-noised speech data results during the training by processing at least a portion of the simulated noisy speech data with the neural network.

14. The system of claim 13 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

determine the decoding error during the training by comparing (1) speech recognition results generated by the ASR system processing the current de-noised speech data results and (2) a transcript of at least a portion of the high quality speech data upon which the at least a portion of the simulated noisy speech data was created.

15. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

collect the high quality speech data in a low noise environment.

16. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

create the simulated noisy speech data by adding reverberation to the high quality speech data using convolution.

17. The system of claim 16 wherein, in adding the reverberation using convolution, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

access a database comprising at least one of: measured impulse responses from a reverberant environment and synthetically generated impulse responses.

18. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

collect data from an environment in which the ASR system is to be deployed; and

create the simulated noisy speech data in accordance with the data collected from the environment.

19. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

perform the training by training the neural network to learn a maximum-likely encryption of the high quality speech data given the simulated noisy speech data.

20. The system of claim 12 wherein the processor and the memory, with the computer code instructions, are further configured to cause the system to:

after the training, process noisy speech data using the trained neural network to determine enhanced speech data.

21. The system of claim 12 wherein, in performing the training on a neural network, the processor and the memory, with the computer code instructions, are further configured to cause the system to:

perform deep normalizing flow training.

22. A non-transitory computer program product for training a neural network for de-noising audio enhancement, the computer program product comprising:

one or more computer-readable storage devices and program instructions stored on at least one of the one or more storage devices, the program instructions, when loaded and executed by a processor, cause an apparatus associated with the processor to:

create simulated noisy speech data from high quality speech data; and

perform training on a neural network using the high quality speech data and the simulated noisy speech data to train the neural network to create de-noised speech data given noisy speech data, wherein performing the training includes minimizing errors in the neural network based on a weighted sum of:

a first differential loss term based on a decoding error of an Automatic Speech Recognition (ASR) system processing current de-noised speech data results that are generated by the neural network during the training; and

a second differential loss term based on spectral distance between the high quality speech data and the current de-noised speech data results that are generated by the neural network during the training.