Automatic generation of statistical language models for interactive voice response applications

• US 20080071533A1
• Filed: 09/14/2006
• Published: 03/20/2008
• Est. Priority Date: 09/14/2006
• Status: Abandoned Application

• Current Assignee: Lymba Corp.
• Original Assignee: Intervoice Limited Partnership, Language Computer Corporation
• Inventors: Balakrishna, Mithun, Cave, Ellis K.
• Application Number:
  US11/522,107
• Publication Number:
  US 20080071533A1
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 2015/0638 Interactive procedures

Abstract

A Statistical Language Model (SLM) that can be used in an ASR for Interactive Voice Response (IVR) systems in general and Natural Language Speech Applications (NLSAs) in particular can be created by first manually producing a brief description in text for each task that can be performed in an NLSA. These brief descriptions are then analyzed, in one embodiment, to generate spontaneous speech utterances based pre-filler patterns and a skeletal set of content words. The pre-filler patterns are in turn used with Part-of-Speech (POS) tagged conversations from a spontaneous speech corpus to generate a set of pre-filler phrases. The skeletal set of content words is used with an electronic lexico-semantic database and with a thesaurus-based content word extraction process to generate a more extensive list of content words. The pre-filler phrases and content words set, thus generated, are combined into utterances using a lexico-semantic resource based process. In one embodiment, a lexico-semantic statistical validation process is used to correct and/or add the automatically generated utterances to the database of expected utterances. The system requires a minimum amount of human intervention and no prior knowledge regarding the expected user utterances, and the WWW is used to validate the word models. The system requires a minimum amount of human intervention and no prior knowledge regarding the expected user utterances in response to a particular prompt.

Claims

1. A method for generating a database of acceptable utterances for use in a speech recognition system, said method comprising:

accepting semantic categories and task descriptions defined by text descriptions;

outputting, based on an accepted one of said categories and description, said category a list of potential utterances that may be spoken by a user to select said one category; and

training a SLM for an ASR system based on said potential utterances.

2. A method for generating a database of acceptable utterance for use in a speech recognition system comprising, said generating occurring without human intervention, said method comprising:

establishing part of speech (POS) patterns for a given prompt;

expanding said POS patterns into possible pre-filler phrases for spontaneous speech; and

eliminating from said possible pre-filler phrases those phrases with a high probability of being inappropriate for said given prompt.

3. The method of claim 2 further comprising:

combining each pre-filler phrase with a skeletal set of words and with a list of closely related words to form a set of alternative utterances; and

using a lexical chain to eliminate from said set of utterance alternatives those utterances that do not have a confidence score above a certain level.

4. The method of claim 3 wherein the POS patterns are expanded automatically.

5. The method of claim 3 wherein the pre-filler phrases are eliminated automatically.

6. The method of claim 3 wherein said expanding comprises:

presenting said POS patterns to a number of POS tagged pre-recorded conversations; and

based on said presenting, extracting said skeletal set of possible pre-filler words for storage in said database.

7. The method of claim 6 further comprising:

presenting said skeletal set of possible words to a thesaurus to obtain said list of closely related words.

8. The method of claim 7 further comprising:

filtering said set of utterances using statistical validation to eliminate those utterances that do not appear in patterns more than a given number of times.

9. The method of claim 8 wherein said statistical validation is a search engine on a general purpose public searchable network.

10. The method of claim 3 further comprising:

evaluating said set of utterances using a WordNet-based process.

11. A method of automatically establishing a set of SLMs for use in an IVR system, said method comprising:

generating for a given IVR prompt an expanded set of possible pre-filler POS phrases based upon manually extracted POS patterns from a relatively small sample of semantic category descriptions;

eliminating inappropriate phrases from said generated set to establish a first level set of POS phrases;

combining each first level pre-filler phases with a skeletal set of content words and with a list of closely related words to form alternative utterances; and

filtering said utterances to achieve a final set of SLMs.

12. The method of claim 11 wherein said eliminating comprises:

presenting said expanded set of POS phrases to POS tagged pre-recorded conversations.

13. The method of claim 12 wherein said pre-recorded conversations comprise SwitchBoard-1 conversations.

14. The method of claim 11 wherein said filtering comprises:

determining from said set of skeletal words an expanded set of words having alternative meanings; and

eliminating from said alternative words those words that are irrelevant in the context of the IVR prompt.

15. The method of claim 14 wherein said alternative meanings are determined using a thesaurus.

16. The method of claim 14 wherein said eliminating comprises:

using lexical paths between word pairs to generate a confidence score; and

eliminating from said expanded set of words those words having a determined low confidence score to create highly relevant content word sequences.

17. The method of claim 16 further comprising:

combining each identified pre-filler sequence with all the content word sequences to create said SLMs.

18. A system for automatically establishing a set of SLMs for use in an IVR system, said system comprising:

means for generating for a given IVR prompt an expanded set of possible pre-filler POS phrases based upon manually extracted POS patterns;

means for eliminating inappropriate phrases from said generated set of phrases;

means for combining said expanded set of possible pre-filler phrases with a skeletal set of words to form utterances; and

means for filtering said utterances to achieve a final set of SLMs.

19. The system of claim 18 wherein said eliminating means comprises:

means for presenting said expanded set of POS phrases to POS tagged pre-recorded conversations.

20. The system of claim 18 wherein said filtering means comprises:

means for determining from said set of skeletal words an expanded set of words having alternative meanings; and

means for eliminating from said alternative set of words those words that are irrelevant in the context of a particular IVR prompt.

21. The system of claim 20 wherein said eliminating means comprises:

means for using a lexical path confidence score for each lexical path to eliminate from said expanded set of words those words having a low confidence score to create highly relevant content word sequences.

22. The system of claim 21 further comprising:

means for combining each identified pre-filler sequence with all the highly relevant content word sequences to create said SLMs.

23. A computer program for automatically establishing a set of SLMs for use in an IVR system, said program comprising:

code for generating for a given IVR prompt an expanded set of possible pre-filler POS phrases based upon manually extracted POS patterns;

code for eliminating inappropriate phrases from said generated set of phrases;

code for combining said expanded set of possible pre-filler phrases with a skeletal set of words to form utterances; and

code for filtering said utterances to achieve a final set of SLMs.

24. The computer program of claim 23 wherein said eliminating code comprises:

code for presenting said expanded set of POS phrases to POS tagged pre-recorded conversations.

25. The computer program of claim 24 wherein said filtering code comprises:

code for determining from said set of skeletal words an expanded set of words having alternative meanings; and

code for eliminating from said alternative set of words those words that are irrelevant in the context of a particular IVR prompt.

26. The computer product of claim 25 wherein said eliminating code comprises:

code for eliminating from said expanded set of words those words having a determined low lexical path confidence score to create highly relevant content word sequences.