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Title: US5956671: Apparatus and methods for shift invariant speech recognition
[ Derwent Title ]


Country: US United States of America

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11 pages

 
Inventor: Ittycheriah, Abraham Poovakunnel; Danbury, CT
Maes, Stephane Herman; Danbury, CT

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 1999-09-21 / 1997-06-04

Application Number: US1997000868860

IPC Code: Advanced: G10L 15/02;
IPC-7: G10L 3/02; G10L 5/06; G10L 9/00;

ECLA Code: G10L15/02; T05K999/99;

U.S. Class: Current: 704/203; 704/231; 704/E15.004;
Original: 704/203; 704/231;

Field of Search: 704/203,231

Priority Number:
1997-06-04  US1997000868860

Abstract:     The present invention includes a method of generating a set of substantially shift invariant acoustic features from an input speech signal which comprises the steps of: splitting the input speech signal into a plurality of input speech signals; respectively delaying a majority of the input speech signals by a successively incrementing time interval; respectively extracting a plurality of sets of acoustic features from the plurality of input speech signals; summing the plurality of sets of acoustic features to form a set of summed acoustic features; and dividing the set of summed acoustic features by a number equivalent to the number of sets of acoustic features summed in the summing step thereby forming a set of averaged acoustic features which are substantially shift invariant. Further, the present invention may include a method for generating at least one substantially shift invariant speech recognition model from speech training data which comprises the steps of: inputting the speech training data a first time; extracting acoustic features from the speech training data input the first time; inputting the speech training data a plurality of times thereafter, each time respectively delaying the input speech training data by a successively incrementing time interval; respectively extracting acoustic features from each delayed speech training data input each time; and utilizing at least the acoustic features extracted in the extracting steps to form the at least one speech recognition model which is substantially shift invariant. Still further, the present invention may include a synchrosqueezing process in the feature extraction steps. Also, the invention contemplates implementing these processes individually, in combination with another of the processes, and a combination of all the processes.

Primary / Asst. Examiners: Hudspeth, David R.; Sax, Robert Louis

Maintenance Status: E1 Expired  Check current status

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Family: None

First Claim:
Show all 18 claims
What is claimed is:     1. A method of generating a set of substantially shift invariant acoustic features from an input speech signal, the method comprising the steps of:
  • (a) splitting the input speech signal into a plurality of input speech signals;
  • (b) respectively delaying a majority of the input speech signals by a successively incrementing time interval;
  • (c) respectively extracting a plurality of sets of acoustic features from the plurality of input speech signals;
  • (d) summing the plurality of sets of acoustic features to form a set of summed acoustic features; and
  • (e) dividing the set of summed acoustic features by a number equivalent to the number of sets of acoustic features summed in step (d) thereby forming a set of averaged acoustic features which are substantially shift invariant.


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Forward References: Show 8 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (8)   |   Backward references (1)   |   Citation Link

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Foreign References: None

Other Abstract Info: DERABS G1999-589480 DERABS G1999-589480

Other References:
  • A Nonlinear Squeezing of the Continuous Wavelet Analysis Based on Auditory Nerve Models, Daubechies et al., Wavelets in Medicine and Biology, edited by A. Alroubi and M. Unser, CRC Press (Jul. 1995, published in Apr. 1996).
  • Robust Speech and Speaker Recognition Using Instantaneous Frequencies and Amplitudes Obtained with Wavelet-Derived Synchrosqueezing Measures, S. Maes, Program on Spline Functions and the Theory of Wavelets, Centre de Recherches Mathematiques, Universite de Montreal, Canada (Mar. 1996).


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