METHOD AND APPARATUS FOR RECOGNIZING SPEECH

• US 20120166194A1
• Filed: 12/22/2011
• Published: 06/28/2012
• Est. Priority Date: 12/23/2010
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: JUNG, Ho-Young, PARK, Jeon-Gue, CHUNG, Hoon
• Application Number:
  US13/335,854
• Publication Number:
  US 20120166194A1
• Time in Patent Office: Days
• US Class Current: 704/238
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/04 Segmentation; Word boundary detection
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]

Abstract

Disclosed herein are an apparatus and method for recognizing speech. The apparatus includes a frame-based speech recognition unit, a segment division unit, a segment feature extraction unit, a segment speech recognition performance unit, and a combination and synchronization unit. The frame-based speech recognition unit extracts frame speech feature vectors from a speech signal, and performs speech recognition on frames of the speech signal using the frame speech feature vectors and a frame-based probability model. The segment division unit divides the speech signal into segments. The segment feature extraction unit extracts segment speech feature vectors around a boundary between the segments. The segment speech recognition performance unit performs speech recognition on the segments of the speech signal using the segment speech feature vectors and a segment-based probability model. The combination and synchronization unit combines results of the speech recognition for the frames with results of the speech recognition for the segments.

Claims

1. A method of recognizing speech, comprising:

extracting frame speech feature vectors from a speech signal;

performing speech recognition on frames of the speech signal using the frame speech feature vectors and a frame-based probability model;

dividing the speech signal into segments each of which is longer than each of the frames in terms of time;

extracting segment speech feature vectors around a boundary between the segments;

performing speech recognition on the segments of the speech signal using the segment speech feature vectors and a segment-based probability model; and

combining results of the speech recognition for the frames with results of the speech recognition for the segments.

2. The method as set forth in claim 1, wherein the dividing comprises calculating a distance measure between adjacent first and second frame speech feature vectors, and, if the calculated distance measure is greater than a predetermined value, dividing the speech signal into the segments using a point between the first and second frame speech feature vectors as a point for the division between the segments.

3. The method as set forth in claim 2, wherein the distance measure is a variation in the speech signal.

4. The method as set forth in claim 1, further comprising synchronizing the results of the speech recognition for the frames with the results of the speech recognition for the segments.

5. The method as set forth in claim 4, wherein the synchronizing comprises applying a Dynamic Bayesian Network (DBN)-based Switching Linear Dynamic Model (SLDM) to a portion where the frame-based probability model is combined with the segment-based probability model in order to synchronize the results of the speech recognition for the frames with the results of the speech recognition for the segments.

6. The method as set forth in claim 1, wherein the extracting segment speech feature vectors comprises extracting the segment speech feature vectors by performing Principal Component Analysis (PCA) and trajectory information feature extraction on the segments of the speech signal.

7. The method as set forth in claim 1, wherein the segment-based probability model is a Gaussian model based on the segment speech feature vectors.

8. The method as set forth in claim 1, wherein the frame-based probability model is a Hidden Markov Model (HMM).

9. An apparatus for recognizing speech, comprising:

a frame-based speech recognition unit for extracting frame speech feature vectors from a speech signal, and performing speech recognition on frames of the speech signal using the frame speech feature vectors and a frame-based probability model;

a segment division unit for dividing the speech signal into segments each of which is longer than each of the frames in terms of time;

a segment feature extraction unit for extracting segment speech feature vectors around a boundary between the segments;

a segment speech recognition performance unit for performing speech recognition on the segments of the speech signal using the segment speech feature vectors and a segment-based probability model; and

a combination and synchronization unit for combining results of the speech recognition obtained by the frame-based speech recognition unit with results of the speech recognition obtained by the segment speech recognition performance unit .

10. The apparatus as set forth in claim 9, wherein the segment division unit calculates a distance measure between adjacent first and second frame speech feature vectors, and, if the calculated distance measure is greater than a predetermined value, divides the speech signal into the segments using a point between the first and second frame speech feature vectors as a point for the division between the segments.

11. The apparatus as set forth in claim 10, wherein the distance measure is a variation in the speech signal.

12. The apparatus as set forth in claim 9, wherein the combination and synchronization unit synchronizes the results of the speech recognition obtained by the frame-based speech recognition unit with the results of the speech recognition obtained by the segment speech recognition performance unit.

13. The apparatus as set forth in claim 12, wherein the combination and synchronization unit applies a DBN-based SLDM to a portion where the frame-based probability model is combined with the segment-based probability model in order to synchronize the results of the speech recognition for the frames with the results of the speech recognition for the segments.

14. The apparatus as set forth in claim 9, wherein the segment extraction unit extracts the segment speech feature vectors by performing PCA and trajectory information feature extraction on the segments of the speech signal.

15. The apparatus as set forth in claim 9, wherein the segment-based probability model is a Gaussian model based on the segment speech feature vectors.

16. The apparatus as set forth in claim 9, wherein the frame-based probability model is an HMM.