Method for microphone selection and multi-talker segmentation with ambient automated speech recognition (ASR)

Patent metadata

Field	Value
Publication number	US-10424317-B2
Application number	US-201715403481-A
Country	US
Kind code	B2
Filing date	Jan 11, 2017
Priority date	Sep 14, 2016
Publication date	Sep 24, 2019
Grant date	Sep 24, 2019

Abstract

Disclosed methods and systems are directed to determining a best microphone pair and segmenting sound signals. The methods and systems may include receiving a collection of sound signals comprising speech from one or more audio sources (e.g., meeting participants) and/or background noise. The methods and systems may include calculating a TDOA and determining, based on the TDOA and via robust statistics, the best pair of microphones. The methods and systems may also include segmenting sound signals from multiple sources.

First claim

What is claimed is:

1. A method comprising:
receiving a plurality of audio signals, wherein each audio signal of the plurality of audio signals is received by one or more pairs of a plurality of microphones;
determining a time delay of arrival (TDOA) for each audio signal corresponding to a difference in receipt time of the plurality of audio signals for the one or more pairs of the plurality of microphones;
clustering the TDOAs to be associated with one of an audio source and interference, resulting in clustering information, wherein at least one TDOA is associated with the audio source and at least one TDOA is associated with the interference; and
segmenting each audio signal of the plurality of audio signals received by the one or more pairs of the plurality of microphones using the clustering information resulting from clustering the TDOAs to identify the audio source.

2. The method of claim 1 , wherein the plurality of microphones comprises at least three microphones, the method further comprising:
performing the clustering for possible pairs of the at least three microphones, resulting in additional clustering information;
generating, based on the additional clustering information, a confidence measure per possible pair of the at least three microphones; and
selecting, based on the confidence measure, one of the possible pairs of microphones.

3. The method of claim 2 , wherein the confidence measure is determined by:
CM l =max P (θ i |τ l ),

where i={1, . . . , N spk }, and
where P(θ i |τ l ), is determined based on a channel selection strategy.

4. The method of claim 1 , wherein the clustering is performed using statistical models.

5. The method of claim 4 wherein the statistical models comprise a Gaussian mixture model (GMM).

6. The method of claim 5 wherein a plurality of input parameters to the GMM are determined at each small time analysis window of a plurality of small time analysis windows and wherein the GMM is determined by:

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Assignees

• Nuance Communications Inc

Inventors

• Parada Pablo Peso
• Sharma Dushyant
• Naylor Patrick

Classifications

• G10L15/00

  Speech recognition (G10L17/00 takes precedence) · CPC title

• G10L25/84

  for discriminating voice from noise · CPC title

• G10L25/03

  characterised by the type of extracted parameters · CPC title

• G10L17/06 Primary

  Decision making techniques; Pattern matching strategies · CPC title

• G10L17/16

  Hidden Markov models [HMM] · CPC title