System and method for processing speech recognition

• US 8,346,550 B2
• Filed: 02/14/2011
• Issued: 01/01/2013
• Est. Priority Date: 10/13/2000
• Status: Expired due to Term

• Current Assignee: Interactions, LLC
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Pathasarathy, Sarangarajan, Rose, Richard C., Rosenberg, Aaron Edward, Narayanan, Shrikanth Sambasivan
• Primary Examiner(s): ALBERTALLI, BRIAN LOUIS
• Application Number:
  US13/026,900
• Publication Number:
  US 20110137651A1
• Time in Patent Office: 687 Days
• Field of Search: None
• US Class Current: 704/238
• 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 mobile 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 performing speech recognition, the method comprising:

receiving a speech utterance, spoken by a user, at a service provider from a client device;

identifying a time associated with the speech utterance;

determining a probability associated with the user and the time, the probability being an expected type of background noise for the user at the time; and

based on the probability, applying a background noise model to recognize the speech utterance.

2. The method of claim 1, wherein the client device is a mobile device.

3. The method of claim 1, wherein determining the probability is based on accessing a stored series of background noises from a user'"'"'s background environment.

4. The method of claim 1, wherein applying the background noise model comprises compensating a speech recognition model with the background noise model.

5. The method of claim 1, wherein determining the probability comprises accessing a profile of the user.

6. The method of claim 1, wherein applying the background speech model is performed based on characteristics of the client device.

7. A non-transitory computer-readable storage medium storing instructions for controlling a computing device to perform speech recognition, the instructions comprising:

receiving a speech utterance, spoken by a user, at a service provider from a client device;

identifying a time associated with the speech utterance;

determining a probability associated with the user and the time, the probability being an expected type of background noise for the user at the time; and

based on the probability, applying a background noise model to recognize the speech utterance.

8. The non-transitory computer-readable storage medium of claim 7, wherein the client device is a mobile device.

9. The non-transitory computer-readable storage medium of claim 7, wherein determining the probability is based on accessing a stored series of background noises from a user'"'"'s background environment.

10. The non-transitory computer-readable storage medium of claim 7, wherein applying the background noise model comprises compensating a speech recognition model with the background noise model.

11. The non-transitory computer-readable storage medium of claim 7, wherein determining the probability comprises accessing a profile of the user.

12. The non-transitory computer-readable storage medium of claim 7, wherein applying the background speech model is performed based on characteristics of the client device.

13. A system for performing speech recognition, the system comprising:

a processor;

a first module configured to control a processor to receive a speech utterance, spoken by a user, at the system from a client device, wherein the client device is remote from the system;

a second module configured to control a processor to identify a time associated with the speech utterance;

a third module configured to control a processor to determine a probability associated with the user and the time, the probability being an expected type of background noise for the user at the time; and

a fourth module configured to control a processor, based on the probability, to apply a background noise model to recognize the speech utterance.

14. The system of claim 13, wherein the client device is a mobile device.

15. The system of claim 13, wherein the third module is further configured to determine the probability based on accessing a stored series of background noises from the user'"'"'s background environment.

16. The system of claim 13, wherein the fourth module further applies the background noise model by compensating a speech recognition model with the background noise model.

17. The system of claim 13, wherein the third module further determines the probability by accessing a profile of the user.

18. The system of claim 13, wherein the fourth module further applies the background noise model is performed based on characteristics of the client device.