SYSTEM AND METHOD FOR PROVIDING A COMPENSATED SPEECH RECOGNITION MODEL FOR SPEECH RECOGNITION

• US 20090063144A1
• Filed: 11/04/2008
• Published: 03/05/2009
• Est. Priority Date: 10/13/2000
• Status: Active Grant

• Current Assignee: Interactions, LLC
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: Pathasarathy, Sarangarajan, Narayanan, Shrikanth Sambasivan, Rose, Richard C., Rosenberg, Aaron Edward
• Granted Patent: US 7,996,220 B2
• Time in Patent Office: Days
• US Class Current: 704/236
• CPC Class Codes: G10L 15/04 Segmentation; Word boundary detection
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/0631 Creating reference templates; Clustering

Abstract

An automatic speech recognition (ASR) system and method is provided for controlling the recognition of speech utterances generated by an end user operating a communications device. The ASR system and method can be used with a communications device that is used in a communications network. The ASR system can be used for ASR of speech utterances input into a mobile device, to perform compensating techniques using at least one characteristic and for updating an ASR speech recognizer associated with the ASR system by determined and using a background noise value and a distortion value that is based on the features of the mobile device. The ASR system can be used to augment a limited data input capability of a mobile device, for example, caused by limited input devices physically located on the mobile device.

Claims

1. A method of recognizing speech, the method comprising:

receiving speech utterances;

determining a probable acoustic environment of the user based on time of day;

compensating a speech recognition model based on the probable acoustic environment; and

recognizing the speech utterances using the compensated speech recognition model.

2. The method according to claim 1, wherein compensating the speech recognition model is further based on a user profile having data related to user vocal information.

3. The method according to claim 2, wherein the user profile further includes transducer data related to a distortion value related to a transducer used in a mobile device.

4. The method according to claim 3, wherein the user profile further includes data related to the acoustic environmental data includes a background noise value that corresponds to an operating environment of a mobile communications device.

5. The method according to claim 1, wherein the probable acoustic environmental is determined from information from a mobile telephone.

6. The method according to claim 1, wherein the data of the particular acoustic environmental is received from a personal digital assistant.

7. The method according to claim 1, wherein the probable acoustic environmental data is determined using at least one microphone in the user'"'"'s environment.

8. The method according to claim 4, wherein the user profile includes data related to a transducer and a distortion value is determined based on a difference between an actual transducer in the mobile device and a response characteristic of a transducer used to train the speech recognition model.

9. The method according to claim 1, further comprising configuring a communications device that receives the speech utterances to select a speech recognition network.

10. The method according to claim 1, further comprising configuring the communications device to select a specific speech recognition network.

11. A tangible computer-readable medium storing instructions for controlling a computing device to recognize speech, the instructions comprising:

receiving speech utterances;

determining a probable acoustic environment of the user based on time of day;

compensating a speech recognition model based on the probable acoustic environment; and

recognizing the speech utterances using the compensated speech recognition model.

12. The tangible computer-readable medium of claim 11, wherein compensating the speech recognition model is further based on a user profile having data related to user vocal information.

13. The tangible computer-readable medium of claim 12, wherein the user profile further includes transducer data related to a distortion value related to a transducer used in a mobile device.

14. The tangible computer-readable medium of claim 13, wherein the user profile further includes data related to the acoustic environmental data includes a background noise value that corresponds to an operating environment of a mobile communications device.

15. The tangible computer-readable medium of claim 11, wherein the probable acoustic environmental is determined from information from a mobile telephone.

16. The tangible computer-readable medium of claim 11, wherein the probable acoustic environment is determined using at least one microphone in the user'"'"'s environment.

17. The tangible computer-readable medium of claim 14, wherein the user profile includes data related to a transducer and a distortion value is determined based on a difference between an actual transducer in the mobile device and a response characteristic of a transducer used to train the speech recognition model.

18. The tangible computer-readable medium of claim 11, wherein in the instructions further comprise updating the speech recognition model and a pronunciation model based on a specific type of mobile communications device.

19. The tangible computer-readable medium of claim 11, further comprising configuring a communications device that receives the speech utterances to select a speech recognition network.

20. A system for recognizing speech, the system comprising:

a module configured to receive the speech utterance;

a module configured to determine a probable acoustic environment of the user based on a time of day;

a module configured to compensate a speech recognition model based on the probable acoustic environment; and

a module configured to recognize the speech utterances using the compensated speech recognition model.