METHOD AND SYSTEM FOR ROBUST PATTERN MATCHING IN CONTINUOUS SPEECH

• US 20090276216A1
• Filed: 05/02/2008
• Published: 11/05/2009
• Est. Priority Date: 05/02/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Kokiopoulou, Effrosyni, Verscheure, Oliver, Frossard, Pascal, Amini, Lisa
• Granted Patent: US 9,293,130 B2
• Time in Patent Office: Days
• US Class Current: 704/236
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A method for speech recognition, the method includes: extracting time—frequency speech features from a series of reference speech elements in a first series of sampling windows; aligning reference speech elements that are not of equal time span duration; constructing a common subspace for the aligned speech features; determining a first set of coefficient vectors; extracting a time—frequency feature image from a test speech stream spanned by a second sampling window; approximating the extracted image in the common subspace for the aligned extracted time—frequency speech features with a second coefficient vector; computing a similarity measure between the first and the second coefficient vector; determining if the similarity measure is below a predefined threshold; and wherein a match between the reference speech elements and a portion of the test speech stream is made in response to a similarity measure below a predefined threshold.

Claims

1. A method for speech recognition in mismatched environments, the method comprising:

extracting time—

frequency speech features from a series of reference speech elements in a first series of sampling windows;

aligning the extracted time—

frequency speech features in response to reference speech elements from the series of speech elements that are not of equal time span duration;

constructing a common subspace for the aligned extracted time—

frequency speech features;

determining a first set of coefficient vectors for the aligned extracted time—

frequency speech features;

extracting a time—

frequency feature image from a test speech stream spanned by a second sampling window;

approximating the extracted time—

frequency feature image in the common subspace for the aligned extracted time—

frequency speech features with a second coefficient vector;

computing a similarity measure between the first set of coefficient vectors and the second coefficient vector;

determining if the similarity measure is below a predefined threshold; and

wherein a match between the reference speech elements and a portion of the test speech stream spanned by the second sampling window is made in response to a similarity measure below a predefined threshold.

2. The method of claim 1, wherein the method further comprises:

incrementing the second sampling window by a unit of time and extracting a new time—

frequency feature image from the test speech stream spanned by the incremented second sampling window;

approximating the extracted new time—

frequency feature image in the common subspace for the aligned extracted time—

frequency speech features with a new coefficient vector;

computing a similarity measure between the first set of coefficient vectors and the new coefficient vector;

determining if the similarity measure is below a predefined threshold;

wherein a match between the reference speech elements and a portion of the test speech stream spanned by the incremented second sampling window is made in response to a similarity measure below a predefined threshold; and

wherein the process of incrementing the second sampling window and computing additional similarity measures continues until the test speech stream is exhausted.

3. The method of claim 1, wherein extracting time—

frequency speech features from the series of reference speech elements is obtained from a feature domain comprising at least one of;

Perceptual linear Predictive (PLP) modified power spectrum, or Mel-Frequency Cepstral Components (MFCC).

4. The method of claim 1, wherein the aligning the extracted time—

frequency speech features employs Dynamic Time Warping (DTW).

5. The method of claim 1, wherein Simultaneous Orthogonal Matching Pursuit (SOMP) is used for constructing the common subspace for the aligned extracted time—

frequency speech features.

6. The method of claim 1, wherein the first set of coefficient vectors are obtained by solving least squares equations.