Speech recognition involving a neural network

• US 20030233233A1
• Filed: 06/13/2002
• Published: 12/18/2003
• Est. Priority Date: 06/13/2002
• Status: Abandoned Application

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Hong, Wei-Tyng
• Application Number:
  US10/167,589
• Publication Number:
  US 20030233233A1
• Time in Patent Office: Days
• US Class Current: 704/256.5
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 25/30 using neural networks

Abstract

Methods and systems for recognizing speech include receiving information reflecting the speech, determining at least one broad-class of the received information, classifying the received information based on the determined broad-class, selecting a model based on the classification of the received information, and recognizing the speech using the selected model and the received information.

Claims

1. A method for recognizing speech, comprising:

receiving information reflecting the speech;

determining at least one broad-class of the received information;

classifying the received information based on the determined broad-class;

selecting a model based on the classification of the received information; and

recognizing the speech using the selected model and the received information.

2. The method of claim 1, wherein the received information comprises extracted feature information.

3. The method of claim 2, wherein the extracted feature information comprises at least one of spectral feature information, temporal feature information, and statistical feature information.

4. The method of claim 1, wherein the determined broad-class is chosen from an initial broad-class, a final broad-class, and a non-speech broad-class.

5. The method of claim 1, wherein the received information comprises information reflecting at least one frame of the speech, wherein determining the broad-class of the received information comprises determining a broad-class of the frame, and wherein classifying the received information does not use the frame if the broad-class of the frame is determined to be an initial broad-class.

6. The method of claim 1, wherein the received information comprises information reflecting at least one frame of the speech, wherein determining the broad-class of the received information comprises determining a broad-class of the frame, and wherein classifying the received information does not use the frame if the broad-class of the frame is determined to be a final broad-class.

7. The method of claim 1, wherein the classification of the received information comprises at least one of a channel classification, an environment classification, and a speaker classification.

8. The method of claim 7, wherein the channel classification comprises at least one of a wireless channel classification and a wired channel classification.

9. The method of claim 7, wherein the environment classification comprises at least one of a quiet office classification, public place classification, and running car classification.

10. The method of claim 1, wherein the selected model is a Hidden Markov Model.

11. The method of claim 1, wherein a recurrent neural network determines the broad-class of the received information.

12. The method of claim 1, wherein a recurrent neural network classifies the received information.

13. A system for recognizing speech, comprising:

a receiver for receiving information reflecting the speech;

a first recurrent neural network for determining at least one broad-class of the received information;

a second recurrent neural network for classifying the received information based on the determined broad-class;

a model selector for selecting a Hidden Markov Model based on the classification of the received information; and

a recognizer for recognizing the speech using the selected Hidden Markov Model and the received information.

14. The system of claim 13, wherein the received information comprises extracted feature information.

15. The system of claim 13, wherein the extracted feature information comprises at least one of spectral feature information, temporal feature information, and statistical feature information.

16. The system of claim 13, wherein the determined broad-class is chosen from an initial broad-class, a final broad-class, and a non-speech broad-class.

17. The system of claim 13, wherein the received information comprises information reflecting at least one frame of the speech, wherein the first recurrent neural network determines a broad-class of the frame, and wherein the second recurrent neural network does not use the frame if the broad-class of the frame is determined to be an initial broad-class.

18. The system of claim 13, wherein the received information comprises information reflecting at least one frame of the speech, wherein the first recurrent neural network determines a broad-class of the frame, and wherein the second recurrent neural network does not use the frame if the broad-class of the frame is determined to be a final broad-class.

19. The system of claim 13, wherein the classification of the received information comprises at least one of a channel classification, an environment classification, and a speaker classification.

20. The system of claim 19, wherein the channel classification comprises at least one of a wireless channel classification and a wired channel classification.

21. The system of claim 19, wherein the environment classification comprises at least one of a quiet office classification, public place classification, and running car classification.

22. A computer-readable medium containing instructions for a computer to perform the steps of:

receiving information reflecting speech;

determining at least one broad-class of the received information;

classifying the received information based on the determined broad-class;

selecting a model based on the classification of the received information; and

recognizing the speech using the selected model and the received information.