METHOD AND SYSTEM FOR SPEECH RECOGNITION

• US 20130311184A1
• Filed: 12/05/2012
• Published: 11/21/2013
• Est. Priority Date: 05/18/2012
• Status: Abandoned Application

• Current Assignee: ASUSTek Computer, Inc.
• Original Assignee: Po-Yuan Yeh
• Inventors: Parng, Tai-Ming, Badavne, Nilay Chokhoba, Yeh, Po-Yuan, Baapanapalli Yadaiah, Vinay Kumar
• Application Number:
  US13/705,168
• Publication Number:
  US 20130311184A1
• Time in Patent Office: Days
• US Class Current: 704/250
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/14 using statistical models, e.g. Hidden Markov Models [HMMs] G10L15/18 takes precedence

Abstract

A method and a system for speech recognition are provided. In the method, vocal characteristics are captured from speech data and used to identify a speaker identification of the speech data. Next, a first acoustic model is used to recognize a speech in the speech data. According to the recognized speech and the speech data, a confidence score of the speech recognition is calculated and it is determined whether the confidence score is over a threshold. If the confidence score is over the threshold, the recognized speech and the speech data are collected, and the collected speech data is used for performing a speaker adaptation on a second acoustic model corresponding to the speaker identification.

Claims

1. A method for speech recognition, comprising:

capturing at least one vocal characteristic from a speech data so as to identify a speaker identification of the speech data;

recognizing a speech in the speech data by using a first acoustic model;

calculating a confidence score of the speech according to the recognized speech and the speech data and determining whether the confidence score is over a first threshold; and

if the confidence score is over the first threshold, collecting the recognized speech and the speech data and performing a speaker adaptation on a second acoustic model corresponding to the speaker identification by using the speech data.

2. The method for speech recognition as recited in claim 1, wherein the step of capturing the at least one vocal characteristic from a speech data so as to identify the speaker identification of the speech data comprises:

recognizing the at least one vocal characteristic by using the second acoustic model that is previously established for each of a plurality of speakers, so as to identify the speaker identification of the speech data according to a recognition transcript of each second acoustic model.

3. The method for speech recognition as recited in claim 1, wherein the step of recognizing the speech in the speech data by using the first acoustic model comprises:

determining whether the speaker identification of the speech data is identified;

if the speaker identification is not identified, creating a new speaker identification and recognizing the speech in the speech data by using a speaker independent acoustic model; and

if the speaker identification is identified, recognizing the speech in the speech data by using the second acoustic model corresponding to the speaker identification.

4. The method for speech recognition as recited in claim 1, wherein the step of calculating the confidence score of the speech according to the recognized speech and the speech data comprises:

estimating the confidence score of the recognized speech by using an utterance verification technique.

5. The method for speech recognition as recited in claim 1, wherein the steps of collecting the recognized speech and the speech data and performing the speaker adaptation on the second acoustic model corresponding to the speaker identification by using the speech data to comprises:

evaluating a pronunciation score of a plurality of utterances in the speech data by using a speech evaluation technique and determining whether the pronunciation score is over a second threshold; and

performing the speaker adaptation on the second acoustic model corresponding to the speaker identification by using all or part of the speech data having the pronunciation score greater than the second threshold.

6. The method for speech recognition as recited in claim 5, wherein the plurality of utterances comprises one of a phoneme, a word, a phrase and a sentence or a combination thereof.

7. The method for speech recognition as recited in claim 1, wherein the step of recognizing the speech in the speech data by using the first acoustic model comprises:

recognizing the speech in the speech data by using an automatic speech recognition (ASR) technique.

8. The method for speech recognition as recited in claim 1, wherein the steps of collecting the recognized speech and the speech data and performing the speaker adaptation on the second acoustic model corresponding to the speaker identification by using the speech data comprises:

determining whether a number of the collected speech data is over a third threshold; and

when the number is over the third threshold, converting a speaker independent acoustic model to a speaker dependent acoustic model serving as the second acoustic model corresponding to the speaker identification by using the collected speech data.

9. The method for speech recognition as recited in claim 1, wherein the first acoustic model and the second acoustic model are Hidden Markov Models (HMMs).

10. A system for speech recognition, comprising:

a speaker identification module, capturing at least one vocal characteristic from a speech data so as to identify a speaker identification of the speech data;

a speech recognition module, recognizing a speech in the speech data by using a first acoustic model;

an utterance verification module, calculating a confidence score of the speech according to the speech recognized by the speech recognition module and the speech data and determining whether the confidence score is over a first threshold;

a data collection module, collecting the speech recognized by the speech recognition module and the speech data when the utterance verification module determines that the confidence score is over the first threshold; and

a speaker adaptation module, performing a speaker adaptation on a second acoustic model corresponding to the speaker identification by using the speech data collected by the data collection module.

11. The system for speech recognition as recited in claim 10, further comprising:

an acoustic model database, recording a plurality of pre-established second acoustic models of a plurality of speakers.

12. The system for speech recognition as recited in claim 11, wherein the speaker identification module recognizes the at least one vocal characteristic by using the plurality of second acoustic models of the plurality of speakers in the acoustic model database, so as to identify the speaker identification of the speech data according to a recognition result of each second acoustic model.

13. The system for speech recognition as recited in claim 12, wherein the speaker identification module further determines whether the speaker identification of the speech data is identified, wherein

if the speaker identification is not identified, a new speaker identification is created, and the speech recognition module recognizes the speech in the speech data by using a speaker independent acoustic model, and

if the speaker identification is identified, the speech recognition module recognizes the speech in the speech data by using the second acoustic model corresponding to the speaker identification.

14. The system for speech recognition as recited in claim 10, wherein the utterance verification module evaluates the confidence score of the recognized speech by using an utterance verification technique.

15. The system for speech recognition as recited in claim 10, further comprising:

a pronunciation scoring module, evaluating a pronunciation score of a plurality of utterances in the speech data by using a speech evaluation technique.

16. The system for speech recognition as recited in claim 15, wherein the speaker adaptation module further determines whether the pronunciation score evaluated by the pronunciation scoring module is over a second threshold, and performs the speaker adaptation on the second acoustic model corresponding to the speaker identification by using all or part of the speech data having the pronunciation score over the second threshold.

17. The system for speech recognition as recited in claim 16, wherein the plurality of utterances comprises one of a phoneme, a word, a phrase and a sentence or a combination thereof.

18. The system for speech recognition as recited in claim 10, wherein the speech recognition module recognizes the speech in the speech data by using an automatic speech recognition (ASR) technique.

19. The system for speech recognition as recited in claim 10, wherein the speaker adaptation module further determines whether a number of the speech data collected by the data collection module is over a third threshold, and converts the speaker independent acoustic model to a speaker dependent acoustic model serving as the second acoustic model corresponding to the speaker identification by using the speech data collected by the data collection module when the number is over the third threshold.

20. The system for speech recognition as recited in claim 10, wherein the first acoustic model and the second acoustic model Hidden Markov Models (HMMs).