Automatic speech clasification

• US 20050049865A1
• Filed: 08/24/2004
• Published: 03/03/2005
• Est. Priority Date: 09/09/2003
• Status: Abandoned Application

• Current Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Original Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Inventors: Fang, Sun, Xin, He, Hao, Tan, Xiao-Lin, Ren, Yaxin, Zhang
• Application Number:
  US10/925,786
• Publication Number:
  US 20050049865A1
• Time in Patent Office: Days
• US Class Current: 704/239
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/228 of application context

Abstract

There is described a method (500) for automatic speech classification performed on an electronic device. The method (500) includes receiving an utterance waveform (520) and processing the waveform (535) to provide feature vectors. Then a step (537) provides for performing speech recognition of the utterance waveform by comparing the feature vectors with at least two sets of acoustic models, one of the sets being a general vocabulary acoustic model set and another of the sets being a digit acoustic model set. The speech recognition step (537) provides candidate strings and associated classification scores from each of the sets of acoustic models. The utterance type is then classified (550) for the waveform based on the classification scores and a selecting step (553) selects one of the candidates as a speech recognition result based on the utterance type. A response is provided (555) depending on the speech recognition result.

Claims

1. A method for automatic speech classification performed on an electronic device, the method comprising:

Receiving an utterance waveform;

processing the waveform to provide feature vectors representing the waveform;

performing speech recognition of the utterance waveform by comparing the feature vectors with at least two sets of acoustic models, one of the sets being a general vocabulary acoustic model set and another of the sets being a digit acoustic model set, the performing providing candidate strings and associated classification scores from each of the sets of acoustic models;

classifying an utterance type for the waveform based on the classification scores;

selecting one of the candidates as a speech recognition result based on the utterance type; and

providing a response depending on the speech recognition result.

2. A method for automatic speech classification as claimed in claim 1, wherein the performing includes:

performing general speech recognition of the feature vectors with the general vocabulary acoustic model set to provide an general vocabulary accumulated maximum likelihood score for word segments of the utterance waveform; and

performing digit speech recognition of the feature vectors with the digit acoustic model set to provide a digit vocabulary accumulated maximum likelihood score for word segments of the utterance waveform.

3. A method for automatic speech classification as claimed in claim 2, wherein the classifying includes evaluating the general vocabulary accumulated maximum likelihood score against the digit vocabulary accumulated maximum likelihood score to provide the utterance type.

4. A method for automatic speech classification as claimed in claim 3, wherein the performing general speech recognition provides a general score, the general score being calculated from a selected number of best accumulated maximum likelihood scores obtained from the performing general speech recognition.

5. A method for automatic speech classification as claimed in claim 4, wherein he performing digit speech recognition provides a digit score, the digit score being calculated from a selected number of best accumulated maximum likelihood scores obtained from the performing digit speech recognition.

6. A method for automatic speech classification as claimed in claim 5, wherein the evaluating also includes evaluating the general score against the digit score to provide the utterance type.

7. A method for automatic speech classification as claimed in claim 3, wherein the processing includes partitioning the waveform into word segments comprising frames, the word segments being analyzed to provide the feature vectors representing the waveform.

8. A method for automatic speech classification as claimed in claim 7, wherein the performing general speech recognition provides an average general broad likelihood score per frame of a word segment.

9. A method for automatic speech classification as claimed in claim 8, wherein the performing digit speech recognition provides an average digit broad likelihood score per frame of a word segment.

10. A method for automatic speech classification as claimed in claim 9, wherein the evaluating also includes evaluating the average general broad likelihood score per frame against the average digit broad likelihood score per frame for the utterance waveform.

11. A method for automatic speech classification as claimed in claim 10, wherein the performing general speech recognition provides an average general speech likelihood score per frame, excluding non-speech frames, of the utterance waveform.

12. A method for automatic speech classification as claimed in claim 11, wherein the performing digit speech recognition provides an average digit speech likelihood score per frame, excluding non-speech frames, of the utterance waveform.

13. A method for automatic speech classification as claimed in claim 12, wherein the evaluating also includes evaluating the average general speech likelihood score per frame against the average digit speech likelihood score per frame to provide the utterance type.

14. A method for automatic speech classification as claimed in claim 13, wherein the performing general speech recognition identifies a maximum general broad likelihood frame score of the utterance waveform.

15. A method for automatic speech classification as claimed in claim 14, wherein the performing digit speech recognition provides a maximum digit broad likelihood frame score of the utterance waveform.

16. A method for automatic speech classification as claimed in claim 15, wherein evaluating also includes evaluating the maximum general broad likelihood frame score against the maximum digit broad likelihood frame score to provide the utterance type.

17. A method for automatic speech classification as claimed in claim 16, wherein the performing general speech recognition identifies a minimum general broad likelihood frame score of the utterance type.

18. A method for automatic speech classification as claimed in claim 17, wherein the performing digit speech recognition provides a minimum digit broad likelihood frame score of the utterance type.

19. A method for automatic speech classification as claimed in claim 18, wherein the evaluating also includes evaluating the minimum general broad likelihood segment score against the minimum general broad likelihood segment score to provide the utterance type.

20. A method for automatic speech classification as claimed in claim 19, wherein the evaluating is performed by a classifier trained on both digit strings and text strings.

21. A method for automatic speech classification as claimed in claim 3, wherein the response includes a control signal for activating a function of the device.

22. A method for automatic speech classification as claimed in claim 21, wherein the response includes a telephone number dialing function when the utterance type is identified as a digit string, wherein the digit sting is a telephone number.