Applying speech recognition adaptation in an automated speech recognition system of a telematics-equipped vehicle

• US 7,725,316 B2
• Filed: 07/05/2006
• Issued: 05/25/2010
• Est. Priority Date: 07/05/2006
• Status: Active Grant

• Current Assignee: General Motors LLC (General Motors Company)
• Original Assignee: General Motors LLC (General Motors Company)
• Inventors: Correia, John J, Pennock, Scott M, Chengalvarayan, Rathinavelu
• Primary Examiner(s): Sked; Matthew J
• Application Number:
  US11/428,663
• Publication Number:
  US 20080010057A1
• Time in Patent Office: 1,420 Days
• Field of Search: None
• US Class Current: 704/234
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 2015/0635 updating or merging of old and new templates; Mean values; Weighting

Abstract

A speech recognition adaptation method for a vehicle having a telematics unit with an embedded speech recognition system. Speech is received and pre-processed to generate acoustic feature vectors, and an adaptation parameter is applied to the acoustic feature vectors to yield transformed acoustic feature vectors. The transformed acoustic feature vectors are decoded and a hypothesis of the speech is selected, and the adaptation parameter is trained using acoustic feature vectors from the hypothesis. The method also includes one or more of the following steps: the speech is observed for a certain characteristic and the trained adaptation parameter is saved in accordance with the certain characteristic for use in transforming feature vectors of subsequent speech having the certain characteristic; use of the trained adaptation parameter persists from one vehicle ignition cycle to the next; and use of the trained adaptation parameter is ceased upon detection of a system fault.

Claims

1. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter; and

(f) ceasing use of the at least one adaptation parameter upon detection of a system fault, wherein the ceasing step (f) comprises;

(1) determining if the at least one trained adaptation parameter has become overtrained; and

(2) resetting the at least one trained adaptation parameter to default values if the at least one trained adaptation parameter has become overtrained.

2. The speech recognition method of claim 1, wherein the determining step includes at least one of detecting divergence of the adaptation process or a predetermined number of speech recognition failures.

3. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter;

(f) pre-training at least one default adaptation parameter based on at least one of vehicle-specific characteristic; and

(g) downloading the at least one pre-trained default adaptation parameter to the vehicle.

4. The speech recognition method of claim 3, wherein the at least one vehicle-specific characteristic includes at least one of vehicle type or noise conditions particular to vehicle type.

5. The method of claim 3 wherein step (f) includes pre-training multiple default adaptation parameters based on the at least one vehicle-specific characteristic and based on at least one user-specific characteristic and step (g) includes downloading the multiple pre-trained default adaptation parameters to the vehicle.

6. The speech recognition method of claim 5, wherein the user-specific characteristics include at least one of user gender, or user dialect based on a region where the vehicle was purchased or is registered.

7. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter; and

(f) observing the speech for a speech type characteristic and saving the at least one trained adaptation parameter in accordance with the speech type characteristic for use in transforming feature vectors of subsequent speech having the speech type characteristic.

8. The speech recognition method of claim 7 wherein step (f) comprises observing the speech for at least one of digit speech or command speech, saving the at least one adaptation parameter associated with the speech as a trained digit speech adaptation parameter when the speech is determined to be digit speech, and saving the at least one adaptation parameter associated with the speech as a trained command speech adaptation parameter when the speech is determined to be command speech.

9. The speech recognition method of claim 7 wherein step (f) comprises observing the speech for at least one of low noise speech or high noise speech, saving the at least one adaptation parameter associated with the speech as a trained low noise speech adaptation parameter when the speech is determined to contain low noise levels, and saving the at least one adaptation parameter associated with the speech as a trained high noise speech adaptation parameter when the speech is determined to contain high noise levels.

10. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter;

(f) observing the speech for a certain characteristic and saving the at least one trained adaptation parameter in accordance with the certain characteristic for use in transforming feature vectors of subsequent speech having the certain characteristic; and

(g) using at least one of an acoustic model or a grammar developed specific to the certain characteristic.

11. The speech recognition method of claim 10, wherein step (g) comprises at least one of observing digit speech in the speech and using an acoustic model developed specific to digit speech, observing command speech in the speech and using an acoustic model developed specific to command speech, or observing a particular language of the speech and using an acoustic model developed specific to that particular language.

12. The speech recognition method of claim 10, wherein step (g) comprises observing nametag training grammar in the speech and using a grammar developed specific to nametag training.

13. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter;

(f) observing the speech for a certain characteristic and saving the at least one trained adaptation parameter in accordance with the certain characteristic for use in transforming feature vectors of subsequent speech having the certain characteristic, wherein the certain characteristic is a speech recognition reliability characteristic including at least one of speech length or SNR level and the method further comprises the steps of;

(g) determining whether the speech recognition reliability characteristic exceeds a minimum value; and

(h) storing the at least one trained adaptation parameter, if the speech recognition reliability characteristic exceeds the minimum value and, if not, then not storing the at least one trained adaptation parameter.

14. A method of speech recognition adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter; and

(f) determining whether a confidence score of the selected hypothesis from the decoding step (d) exceeds a minimum confidence score; and

(g) storing the at least one trained adaptation parameter associated with the speech, if the confidence score of the selected hypothesis exceeds the minimum confidence score and, if not, then not storing the at least one trained adaptation parameter.

15. A method of speech recognition with characteristic-specific adaptation for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech;

(b) pre-processing the speech to generate acoustic feature vectors;

(c) applying at least one adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech;

(e) training the at least one adaptation parameter with acoustic feature vectors of the selected hypothesis to yield at least one trained adaptation parameter; and

(f) observing the speech for a certain characteristic and saving the at least one trained adaptation parameter in accordance with the certain characteristic for use as at least one trained adaptation parameter in transforming feature vectors of subsequent speech having the certain characteristic, wherein the certain characteristic is speech type, such that step (f) comprises observing the speech for speech type including at least one of digit speech or command speech, saving the trained adaptation parameters associated with the speech as trained digit speech adaptation parameters when the speech is determined to be digit speech, and saving the trained adaptation parameters associated with the speech as trained command speech adaptation parameters when the speech is determined to be a command speech.

16. The method of claim 15, wherein the decoding step (d) further comprises using at least one of an acoustic model or a grammar developed specific to the certain characteristic of the speech.

17. The method of claim 15, wherein step (f) comprises observing the speech for user identity and using one or more adaptation parameters associated with the identity of the user.

18. A method of speech recognition with persistence of adaptation parameters for a vehicle having a telematics unit with an embedded speech recognition system, the method comprising the steps of:

(a) receiving speech after an ignition event of the vehicle;

(b) pre-processing a segment of the speech to generate acoustic feature vectors;

(c) applying an adaptation parameter to the acoustic feature vectors to yield transformed acoustic feature vectors;

(d) decoding the transformed acoustic feature vectors to select a hypothesis therefrom corresponding to the received speech; and

(e) training the adaptation parameter with acoustic feature vectors of the selected hypothesis and saving the trained adaptation parameter for use in transforming feature vectors of another segment of speech received after a subsequent vehicle ignition event such that the trained adaptation parameter persists over vehicle ignition events so that the adaptation parameter need not be retrained upon every vehicle ignition event.