System and method for efficient storage of voice recognition models

• US 20040098258A1
• Filed: 11/12/2003
• Published: 05/20/2004
• Est. Priority Date: 01/12/2001
• Status: Active Grant

• Current Assignee: Harinath Garudadri
• Original Assignee: Harinath Garudadri
• Inventors: Garudadri, Harinath
• Granted Patent: US 7,136,815 B2
• Time in Patent Office: Days
• US Class Current: 704/249
• CPC Class Codes: G10L 15/06 Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice G10L15/14 takes precedence

Abstract

A method and system that improves voice recognition by improving storage of voice recognition (VR) templates. The improved storage means that more VR models can be stored in memory. The more VR models that are stored in memory, the more robust the VR system and therefore the more accurate the VR system. Lossy compression techniques are used to compress VR models. In one embodiment, A-law compression and A-law expansion are used to compress and expand VR models. In another embodiment, Mu-law compression and Mu-law expansion are used to compress and expand VR models. VR models are compressed during a training process and they are expanded during voice recognition.

Claims

1. A method of voice recognition (VR), comprising:

recording a plurality of utterances;

extracting features of the plurality of utterances to generate extracted features of the plurality of utterances;

creating a plurality of VR models from the extracted features of the plurality of utterances; and

lossy-compressing the plurality of VR models to generate a plurality of lossy-compressed VR models.

2. The method of claim 1, further comprising expanding the plurality of lossy-compressed VR models to generate a plurality of expanded VR models.

3. The method of claim 2, further comprising comparing a expanded VR model of the plurality of expanded VR models to features of a test utterance.

4. The method of claim 2, further comprising extracting features of a test utterance.

5. The method of claim 4, further comprising matching the extracted features of the test utterance to an expanded VR model of the plurality of expanded VR models to generate a match.

6. The method of claim 5, further comprising generating a hypothesis for the match.

7. The method of claim 1, wherein the lossy-compression is A-law compression of the plurality of VR models to generate a plurality of A-law compressed VR models.