SEARCH OPTIMIZATION FOR CONTINUOUS SPEECH RECOGNITION

• US 20010041978A1
• Filed: 11/04/1998
• Published: 11/15/2001
• Est. Priority Date: 12/24/1997
• Status: Active Grant

• Current Assignee: Popkin Family Assets LLC (Intellectual Ventures LLC)
• Original Assignee: Nortel Networks Limited (Nortel Networks Corporation)
• Inventors: STUBLEY, PETER R., ROBILLARD, SERGE, CRESPO, JEAN-FRANCOIS
• Granted Patent: US 6,397,179 B2
• Time in Patent Office: Days
• US Class Current: 704/257
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 2015/085 Methods for reducing search complexity, pruning

Abstract

A system and method for continuous speech recognition (CSR) is optimized to reduce processing time for connected word grammars bounded by semantically null words. The savings, which reduce processing time both during the forward and the backward passes of the search, as well as during rescoring, are achieved by performing only the minimal amount of computation required to produce an exact N-best list of semantically meaningful words (N-best list of salient words). This departs from the standard Spoken Language System modeling which any notion of meaning is handled by the Natural Language Understanding (NLU) component. By expanding the task of the recognizer component from a simple acoustic match to allow semantic information to be fed to the recognizer, significant processing time savings are achieved, and make it possible to run an increased number of speech recognition channels in parallel for improved performance, which may enhance users perception of value and quality of service.

Claims

1. A method for continuous speech recognition comprising:

incorporating semantic information during searching by a continuous speech recognizer.

2. A method for continuous speech recognition according to

claim 1

, comprising searching using semantic information to identify semantically-null words and thereby generate a list of N-best salient words.

14. A system for continuous speech recognition according to

claim 1

, comprising means for searching using semantic information to generate a list of N-best salient words.

3. A method for continuous speech recognition

providing speech input to a continuous speech recognizer,

providing to the continuous speech recognizer an acoustic model comprising a set of Hidden Markov Models, and a language model comprising both grammar and semantic information,

performing recognition of speech input using semantic information to eliminate semantically null words from the N-best list of words and restrict searching to an N-best list of salient words,

and performing word matching to output from the speech recognizer the N-best salient word sequences.

4. A method for a continuous speech recognition process according to

claim 3

wherein the step of performing recognition comprises:

detecting connected word grammars bounded by semantically null words;

collapsing each list of semantically null words into a unique single-input single-output acoustic network;

and identifying stop nodes in the acoustic network.

5. A method according to

claim 4

comprising:

during a forward pass of a search detecting forward stop nodes and signalling the search to stop forward scoring along a path currently being followed, and

during a backward pass of the search detecting backwards stop nodes and signalling the search to stop backward scoring along a path currently being followed.

6. A method according to 5 wherein right-most semantically null networks are not computed.

7. A method according to 5 wherein some semantically salient words are not backward-scored.

8. A method according to 5 wherein an N-best list of only salient words is rescored instead of a true N-best list.

9. A method according to

claim 8

wherein scoring comprises Viterbi scoring.

10. Software on a machine readable medium for performing a method of continuous speech recognition comprising:

incorporating semantic information during searching by a continuous speech recognizer.

11. Software for performing a method of continuous speech recognition according to

claim 10

, wherein the method comprises searching using semantic information to generate a list of N-best salient words.

12. Software on a machine readable medium for performing a method for continuous speech recognition

providing speech input to a continuous speech recognizer,

providing to the continuous speech recognizer an acoustic model comprising a set of Hidden Markov Models, and a language model comprising both grammar and semantic information,

performing recognition of speech input using semantic information to eliminate semantically null words from the N-best list of words and restrict searching to an N-best list of salient words,

13. A system for continuous speech recognition comprising:

means for incorporating semantic information during searching by a continuous speech recognizer.

15. A system for continuous speech recognition

comprising a continuous speech recognizer,

input means for providing speech input to the continuous speech recognizer,

means for providing to the continuous speech recognizer an acoustic model comprising a set of Hidden Markov Models, and a language model comprising both grammar and semantic information,

the continuous speech recognizer comprising means for performing recognition of speech input using the semantic information for eliminating semantically null words from the N-best list of words and thereby restricting searching to an N-best list of salient words, and performing word matching to output the N-best salient word sequences.

16. A system according to

claim 15

means for performing recognition of speech input using the semantic information comprises:

means for detecting connected word grammars bounded by semantically null words;

means for collapsing each list of semantically null words into a unique single-input single-output acoustic network;

and means for identifying stop nodes in the acoustic network.

17. A spoken language processing system for speech recognition comprising:

a continuous speech recognition component (CSR)

a natural language understanding component (NLU)

means for providing speech input to the CSR,

means for providing acoustic-phonetic knowledge to the CSR comprising a set of Hidden Markov Models;

means for providing language knowledge comprising grammar and statistical models to the CSR, and means for providing semantic knowledge the NLU, and

means for providing semantic knowledge to the CSR,

the CSR being operable for searching using the semantic knowledge to constrain the search to an N-best list of salient words, and perform word matching to output N-best list of salient words to the NLU for interpretation of meaning.

18. A method for continuous speech recognition using a spoken language system comprising a continuous speech recognition component (CSR) linked to a natural language understanding component (NLU)

providing speech input to the CSR

providing acoustic-phonetic knowledge to the CSR comprising a set of Hidden Markov Models;

providing language knowledge comprising grammar and statistical models to the CSR;

providing language knowledge semantic knowledge to the CSR;

performing searching with the CSR using the semantic knowledge to constrain the search to an N-best list of salient words comprising semantically meaningful words of the N-best list of words,

and performing word matching to output the N-best salient word sequences to the NLU.