Multichannel speech recognition using neural networks

Patent metadata

Field	Value
Publication number	US-11062725-B2
Application number	US-201916278830-A
Country	US
Kind code	B2
Filing date	Feb 19, 2019
Priority date	Sep 7, 2016
Publication date	Jul 13, 2021
Grant date	Jul 13, 2021

Abstract

This specification describes computer-implemented methods and systems. One method includes receiving, by a neural network of a speech recognition system, first data representing a first raw audio signal and second data representing a second raw audio signal. The first raw audio signal and the second raw audio signal describe audio occurring at a same period of time. The method further includes generating, by a spatial filtering layer of the neural network, a spatial filtered output using the first data and the second data, and generating, by a spectral filtering layer of the neural network, a spectral filtered output using the spatial filtered output. Generating the spectral filtered output comprises processing frequency-domain data representing the spatial filtered output. The method still further includes processing, by one or more additional layers of the neural network, the spectral filtered output to predict sub-word units encoded in both the first raw audio signal and the second raw audio signal.

First claim

What is claimed is:

1. A method comprising:
receiving, at data processing hardware, a multi-channel audio input comprising a first audio signal and a second audio signal occurring during a same period of time;
obtaining, by the data processing hardware, a first time-domain representation of the first audio signal and a second time-domain representation of the second audio signal;
generating, by the data processing hardware, using a spatial filtering convolutional layer of a neural network configured to perform spatial filtering, a corresponding spatial filtered output for each of multiple spatial directions by processing the first time-domain representation of the first audio signal and the second time-domain representation of the second audio signal, the spatial filtering convolutional layer using a stride parameter being set to an integer greater than one;
converting, by the data processing hardware, the corresponding spatial filtered output generated for each of the multiple spatial directions into corresponding frequency-domain data; and
processing, by the data processing hardware, using one or more additional neural network layers of the neural network, the corresponding frequency-domain data converted from the spatial filtered output generated for each of the multiple spatial directions to predict speech content encoded in the first audio signal and the second audio signal.

2. The method of claim 1 , wherein converting the corresponding spatial filtered output generated for each of the multiple spatial directions into corresponding frequency-domain data comprises computing a discrete Fourier transform for the corresponding spatial filtered output generated for each of the multiple spatial directions.

3. The method of claim 2 , wherein computing the discrete Fourier transform for the corresponding spatial filtered output generated for each of the multiple spatial directions comprises computing a fast Fourier transform for the corresponding spatial filtered output generated for each of the multiple spatial directions.

4. The method of claim 1 , wherein the neural network is part of a speech recognition model.

5. The method of claim 1 , wherein the neural network is part of an acoustic model configured to indicate probabilities of sub-word units.

6. The method of claim 1 , wherein the one or more additional neural network layers comprise one or more deep neural network layers that provide output to one or more long short-term memory layers.

7. The method of claim 1 , wherein the corresponding spatial filtered output generated for each of the multiple spatial directions comprises a single channel of time-domain data.

8. The method of claim 1 , wherein at least one additional neural network layer of the one or more additional neural network layers is configured to perform feature extraction.

9. The method of claim 8 , wherein the at least one additional neural network layer of the one or more additional neural network layers that is configured to perform feature extraction is also configured to apply a transformation to the corresponding frequency-domain data converted from the spatial filtered output generated for each of the multiple spatial directions.

10. The method of claim 9 , wherein the transformation is a linear transformation.

11. The method of claim 9 , wherein the transformation is a projection.

12. The method of claim 9 , wherein the transformation is a complex linear projection.

13. The method of claim 9 , wherein the transformation is a linear projection of energy.

14. The method of claim 1 , wherein the neural network comprises:
the spatial filtering convolutional layer;
at least one feature extraction neural network layer configured to determine frequency-based characteristics of the corresponding frequency-domain data converted from the spatially filtered output generated for each of the multiple spatial directions; and
one or more neural network layers configured to receive output of the at least one feature extraction neural network layer and determine speech content using one or more recurrent neural network layers and one or more deep neural network layers.

15. The method of claim 1 , further comprising:
detecting, by the data processing hardware, the first audio signal and the second audio signal using multiple microphones of a computing device,
wherein the data processing hardware resides on the computing device.

16. The method of claim 1 , further comprising:
detecting, by the data processing hardware, the first audio signal and the second audio signal using multiple microphones of a computing device;
wherein the neural network is stored or implemented on the computing device.

17. The method of claim 1 , wherein processing the corresponding frequency-domain data converted from the spatial filtered output generated for each of the multiple spatial directions comprises identifying a voice command indicated by the first audio signal and the second audio signal.

18. The method of claim 1 , wherein the spatial filtering convolutional layer and the one or more additional layers have been jointly trained during training of the neural network.

19. A system comprising:
one or more computing devices; and
one or more computer-readable media storing instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving a multi-channel audio input comprising a first audio signal and a second audio signal occurring during a same period of time;
obtaining a first time-domain representation of the first audio signal and a second time-domain representation of the second audio signal;
generating, using a spatial filtering convolutional layer of a neural network configured to perform spatial filtering, a corresponding spatial filtered output for each of multiple spatial directions by processing the first time-domain representation of the first audio signal and the second time-domain representation of the second audio signal, the spatial filtering convolutional layer using a stride parameter being set to an integer greater than one;
converting the corresponding spatial filtered output generated for each of the multiple spatial directions into corresponding frequency-domain data; and
processing, using one or more additional neural network layers of the neural network, the corresponding frequency-domain data converted from the spatial filtered output generated for each of the multiple spatial directions to predict speech content encoded in the first audio signal and the second audio signal.

20. One or more non-transitory computer-readable media storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving a multi-channel audio input comprising a first audio signal and a second audio signal occurring during a same period of time;
obtaining a first time-domain representation of the first audio signal and a second time-domain representation of the second audio signal;
generating, using a spatial filtering convolutional layer of a neural network configured to perform spatial filtering, a corresponding spatial filtered output for each of multiple spatial directions by processing the first time-domain representation of the first audio signal and the second time-domain representation of the second audio signal, the spatial filtering convolutional layer using a stride parameter being set to an integer greater than one;
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Assignees

• Google Llc

Inventors

• Variani Ehsan
• Wilson Kevin William
• Weiss Ron J
• Sainath Tara N
• Narayanan Arun

Classifications

• G10L21/028 Primary

  using properties of sound source · CPC title

• G10L21/0388

  Details of processing therefor · CPC title

• G10L2021/02166

  Microphone arrays; Beamforming · CPC title

• G10L2021/02087

  the noise being separate speech, e.g. cocktail party · CPC title

• G10L19/008

  Multichannel audio signal coding or decoding using interchannel correlation to reduce redundancy, e.g. joint-stereo, intensity-coding or matrixing · CPC title