LANGUAGE MODEL ADAPTATION USING RESULT SELECTION

• US 20140365218A1
• Filed: 06/07/2013
• Published: 12/11/2014
• Est. Priority Date: 06/07/2013
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chang, Shuangyu, Levit, Michael
• Application Number:
  US13/913,032
• Publication Number:
  US 20140365218A1
• Time in Patent Office: Days
• US Class Current: 704/244
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
• CPC Class Codes: G10L 15/32 Multiple recognisers used in sequence or in parallel; Score combination systems therefor, e.g. voting systems

Abstract

A received utterance is recognized using different language models. For example, recognition of the utterance is independently performed using a baseline language model (BLM) and using an adapted language model (ALM). A determination is made as to what results from the different language model are more likely to be accurate. Different features may be used to assist in making the determination (e.g. language model scores, recognition confidences, acoustic model scores, quality measurements, . . . ) may be used. A classifier may be trained and then used in determining whether to select the results using the BLM or to select the results using the ALM. A language model may be automatically trained or re-trained that adjusts a weight of the training data used in training the model in response to differences between the two results obtained from applying the different language models.

Claims

1. A method using results from different language models, comprising:

receiving language model results including a language model recognition output in response to performing recognition on an utterance using a language model;

receiving adapted language model results including an adapted language model recognition output in response to performing recognition on the utterance using an adapted language model;

selecting the language model results in response to an automatic determination that determines that the language model results are more likely to be accurate as compared to the adapted language model results; and

selecting the adapted language model results in response to the automatic determination that determines that the adapted language model results are more likely to be accurate as compared to the language model results.

2. The method of claim 1, further comprising extracting features from the language model results and extracting features from the adapted language model results, including determining a language model score and an adapted language model score.

3. The method of claim 1, further comprising selecting the language model results before performing the recognition on the utterance using the adapted language model in response to an automatic determination that the language model results are accurate and selecting the adapted language model results before performing the recognition on the utterance using the language model in response to an automatic determination that the adapted language model results are accurate.

4. The method of claim 1, wherein the automatic determination comprises applying a statistical classifier to features extracted from the language model results and the adapted language model results.

5. The method of claim 1, further comprising extracting features from the language model results and extracting features from the adapted language model results including extracting at least one of:

a recognition confidence associated with the recognition using the language model and the recognition using the adapted language model;

or quality measurements of audio data of the utterance.

6. The method of claim 1, wherein the automatic determination comprises computing a log likelihood score using the language model recognition output and the adapted language recognition output.

7. The method of claim 1, wherein performing the recognition on the utterance using the language model and performing the recognition on the utterance using the adapted language model occurs in parallel.

8. The method of claim 1, further comprising training a language model using utterances weighted differently in response to the language model results being selected.

9. The method of claim 8, further comprising computing statistics including determining Ngram differences using the language model results and the adapted language model results to determine weights to associate with the utterances.

10. A computer-readable medium storing computer-executable instructions for using results from different language models, comprising:

performing recognition on an utterance using different language models;

receiving results associated with each performed recognition using the different language models;

extracting features from the results including determining language model scores associated with each of the different language models; and

selecting results associated with one of the different language models in response to an automatic determination using a statistical classifier applied to the results.

11. The computer-readable medium of claim 10, wherein the different language models consist of a baseline language model and an adapted language model.

12. The computer-readable medium of claim 10, wherein the statistical classifier is trained using features extracted from language model results and adapted language model results.

13. The computer-readable medium of claim 10, wherein extracting the features comprises determining an acoustic model score and determining quality measurements of audio data of the utterance.

14. The computer-readable medium of claim 10, wherein selecting the results comprises determining a recognition confidence associated with each of the different language models.

15. The computer-readable medium of claim 10, wherein performing the recognition on the utterance using the different language models occurs in parallel.

16. The computer-readable medium of claim 10, further comprising retraining an adapted language model using utterances in training data weighted differently in response to statistics determined from the results received associated with the different language models.

17. A system for using results from different language models, comprising:

a processor and memory;

an operating environment executing using the processor; and

a model manager that is configured to perform actions comprising;

receiving an utterance;

performing recognition on the utterance using a language model and an adapted language model;

receiving language model results and adapted language model results in response to performing the recognition on the utterance using the language model and the adapted language model;

extracting features from the language model results and the adapted language model results, comprising a language model score and an adapted language model score that each indicate a likelihood of the result given the associated language model; and

determining when to select the language model results and when to select the adapted language model results using a statistical classifier.

18. The system of claim 17, wherein extracting the features comprises determining recognition confidences associated with performing the recognition on the utterance using the language model and performing the recognition on the utterance using the adapted language model.

19. The system of claim 17, further comprising training a language model using utterances in a training data set weighted differently in response to differences between language model results and adapted language model results.

20. The system of claim 17, further comprising retraining the adapted language model with the utterance weighted differently in response to the language model results being selected;

and determining Ngram differences between the language model results and the adapted language model results.