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Title: US5953701: Speech recognition models combining gender-dependent and gender-independent phone states and using phonetic-context-dependence
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Country: US United States of America

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9 pages

 
Inventor: Neti, Chalapathy Venkata; Yorktown Heights, NY
Roukos, Salim Estephan; Scarsdale, NY

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 1999-09-14 / 1998-01-22

Application Number: US1998000010466

IPC Code: IPC-7: G10L 5/06;

ECLA Code: G10L15/07; G10L15/14M;

U.S. Class: Current: 704/254; 704/240; 704/242; 704/252;
Original: 704/254; 704/242; 704/240; 704/252;

Field of Search: 704/240,242,252,254

Priority Number:
1998-01-22  US1998000010466

Abstract:     A method of gender dependent speech recognition includes the steps of identifying phone state models common to both genders, identifying gender specific phone state models, identifying a gender of a speaker and recognizing acoustic data from the speaker. A method of constructing a gender-dependent speech recognition model includes the steps of providing training data of a known gender, aligning the training data, tagging the training data with a gender to create gender-tagged data, determining a gender question at a node to determine gender dependence of the gender-tagged data, determining a phonetic context question at the node to determine phonetic context dependence of the gender-tagged data, determining a highest value of an evaluation function between the gender dependence and the phonetic context dependence to determine which dependence is a dominant dependence, splitting the data of the dominant dependence into child nodes according to likelihood criteria, comparing the highest value with a threshold value to determine if additional splitting is necessary, repeating theses steps for each child node until the highest value is below the threshold value and counting the nodes having gender dependence to determine an overall gender dependence level. A gender-dependent speech recognition system includes an input device for inputting speech to a preprocessor. The preprocessor converts the speech into acoustic data, and a processor for identifies gender-dependent phone state models and phone state modes common to both genders. The phone state models are stored in a memory device wherein the processor recognizes the speech in accordance with the phone state models.

Attorney, Agent or Firm: Tassinari, Jr., Robert P. ;

Primary / Asst. Examiners: Hudspeth, David R.; Smits, Talivaldis Ivars

Maintenance Status: E2 Expired  Check current status

INPADOC Legal Status: Show legal status actions

Family: None

First Claim:
Show all 18 claims
What is claimed is:     1. A method of gender dependent speech recognition comprising the steps of:
  • identifying phone state models common to both genders;
  • identifying gender specific phone state models;
  • identifying a gender of a speaker; and
  • recognizing acoustic data from the speaker based on the phone state models.


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Forward References: Show 43 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (43)   |   Backward references (3)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 15pp US5675705  1997-10 Singhal   Spectrogram-feature-based speech syllable and word recognition using syllabic language dictionary
Get PDF - 12pp US5787394  1998-07 Bahl et al.  International Business Machines Corporation State-dependent speaker clustering for speaker adaptation
Get PDF - 12pp US5825978  1998-10 Digalakis et al.  SRI International Method and apparatus for speech recognition using optimized partial mixture tying of HMM state functions
       
Foreign References: None

Other References:
  • L. R. Bahl et al., "Decision Trees for Phonological Rules in Continuous Speech", S3.9, ©1991 IEEE. pp. 185-188.
  • L. R. Bahl et al, "Robust Methods For Using Context-Dependent Features And Models In a Continuous Speech Recognizer.", ©1994 IEEE, pp. I-533-I-5336.
  • Chalapathy V. Neti et al., "Word-Based Confidence Measures As A Guide For Stack Search In Speech Recognition", ©1997 IEEE, pp. 883-886.


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