Generation of domain models from noisy transcriptions

• US 20080091423A1
• Filed: 10/13/2006
• Published: 04/17/2008
• Est. Priority Date: 10/13/2006
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Subramaniam, Laxminarayan Venkata, Roy, Shourya
• Application Number:
  US11/549,173
• Publication Number:
  US 20080091423A1
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

A method of building a domain specific model from transcriptions is disclosed. The method starts by applying text clustering to the transcriptions to form text clusters. The text clustering is applied at a plurality of different granularities, and groups topically similar phrases in the transcriptions. The relationship between text clusters resulting from the text clustering at different granularities is then identified to form a taxonomy. The taxonomy is augmented with topic specific information.

Claims

1. A method of building a domain specific model from transcriptions, said method comprising the steps of:

applying text clustering to said transcriptions to form text clusters, where said text clustering is applied at a plurality of different granularities and groups topically similar phrases in said transcriptions;

identifying the relationship between text clusters resulting from said text clustering at different granularities to form a taxonomy; and

augmenting said taxonomy with topic specific information.

2. The method according to claim 1 wherein said taxonomy is augmented with one or more of:

typical issues raised and solutions to those issues;

typical questions and answers; and

statistics relating to conversations associated with said transcriptions.

3. The method according to claim 1 comprising the initial step of:

extracting from said transcriptions n-grams based upon the respective frequencies of occurrences of the n-grams.

4. The method according to claim 1 wherein said identifying step identifies relationships between text clusters resulting from said text clustering at granularities of adjacent levels.

5. The method according to claim 1 comprising the initial steps of:

removing stopwords from said transcriptions; and

removing pause filling words from said transcriptions.

6. The method according to claim 5 wherein said stopwords include generic stopwords and domain specific stopwords.

7. A method comprising the steps of:

receiving a domain specific model;

receiving a transcription of a part of a conversation; and

mapping said transcription to a node in said domain specific model.

8. A method comprising the steps of:

receiving a domain specific model;

receiving a transcription of a conversation;

calculating statistics of said transaction; and

comparing at least said statistics of said transcription with statistics from said domain specific model.

9. An apparatus for building a domain specific model from transcriptions, said apparatus comprising a processor configured to perform the steps comprising of:

applying text clustering to said transcriptions to form text clusters, where said text clustering is applied at a plurality of different granularities and groups topically similar phrases in said transcriptions;

identifying the relationship between text clusters resulting from said text clustering at different granularities to form a taxonomy; and

augmenting said taxonomy with topic specific information.

10. The apparatus according to claim 9 wherein said taxonomy is augmented with one or more of:

typical issues raised and solutions to those issues;

typical questions and answers; and

statistics relating to conversations associated with said transcriptions.

11. The apparatus according to claim 9 wherein said processor is configured to perform the further step of:

extracting from said transcriptions n-grams based upon the respective frequencies of occurrences of the n-grams.

12. The apparatus according to claim 9 wherein said identifying step identifies relationships between text clusters resulting from said text clustering at granularities of adjacent levels.

13. An apparatus configured to perform the steps comprising of:

receiving a domain specific model;

receiving a transcription of a part of a conversation; and

mapping said transcription to a node in said domain specific model.

14. An apparatus configured to perform the steps comprising of:

receiving a domain specific model;

receiving a transcription of a conversation;

calculating statistics of said transaction; and

comparing at least said statistics of said transcription with statistics from said domain specific model.