Efficient Speech Recognition with Cluster Methods

• US 20080300875A1
• Filed: 06/04/2008
• Published: 12/04/2008
• Est. Priority Date: 06/04/2007
• Status: Abandoned Application

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Yao, Kaisheng, Tsao, Yu
• Application Number:
  US12/133,197
• Publication Number:
  US 20080300875A1
• Time in Patent Office: Days
• US Class Current: 704/236
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]

Abstract

A speech recognition method and system, the method comprising the steps of providing a speech model, said speech model includes at least a portion of a state of Gaussian, clustering said Gaussian of said speech model to give N clusters of Gaussians, wherein N is an integer and utilizing said Gaussian in recognizing an utterance.

Claims

1. A speech recognition method, comprising the steps of:

providing a speech model, said speech model includes at least a portion of a state of Gaussian;

clustering said Gaussian of said speech model to give N clusters of Gaussians, wherein N is an integer; and

utilizing said Gaussian in recognizing an utterance.

2. The speech recognition method of claim 1, wherein the step of recognizing said utterance comprising:

compensating said Gaussian for distortion resulting in a compensated Gaussian, where said compensating derives from a cluster containing said Gaussian; and

using said compensated Gaussian for compensated models for recognition of an utterance.

3. The speech recognition method of claim 1 further comprising the steps of:

estimating said distortion after recognition of a first utterance; and

using said estimation for the recognition of a second utterance.

4. A speech recognition method of claim 1, wherein the step recognizing said utterance comprising:

providing an utterance, said utterance corresponding to a feature;

for at least one portion of said feature, categorizing said Gaussians into one of M categories, wherein M is an integer, according to which of said clusters contains said Gaussian by using measurement of distance from said feature to said cluster; and

when said Gaussian is in a first of said M categories, evaluating said Gaussian for said feature, and when said Gaussian is in a second of said M categories, approximating said Gaussian for said feature according to the cluster containing said Gaussian.

5. A speech recognition method of claim 1, wherein the step recognizing said utterance comprising:

receiving a leading frame of non-speech of a received utterance;

for said leading frames, selecting a corresponding one of said N cluster which has the largest probability for observation of said leading frame;

for a subsequent frame received after said leading frame, computing a probability of observing said subsequent frame for any of said corresponding cluster; and

using said probability as adjunct to probability of background or silence model.

6. A speech recognition method of claim 1, wherein the step recognizing said utterance comprising:

receiving a leading frame of non-speech of a received utterance;

for said leading frame, selecting a corresponding one of said N cluster which has the largest probability for observation of said each leading frame;

for a subsequent frame received after said plurality of leading frames, computing a ratio of a probability of observing said subsequent frame for any of said N clusters divided by a probability of observing said subsequent frame for any of said corresponding cluster; and

using said ratio in speech detection.

7. An automatic speech recognition system, comprising:

utterance receiving mechanism;

a speech model access mechanism, said speech model includes at least a portion of a state of Gaussian; and

a computer readable medium comprising computer instructions that, when executed by a processor, causes the processor to perform a method comprising;

clustering said Gaussian of said speech model, retrieved view said speech model access mechanism, to give N clusters of Gaussian, wherein N is an integer; and

utilizing said Gaussian in recognizing said utterance, from said utterance receiving mechanism.