Environmently compensated speech processing

• US 5,924,065 A
• Filed: 06/16/1997
• Issued: 07/13/1999
• Est. Priority Date: 06/16/1997
• Status: Expired due to Term

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Digital Equipment Corporation (HP Inc.)
• Inventors: Moreno, Pedro J., Eberman, Brian S.
• Primary Examiner(s): Hudspeth, David R.
• Assistant Examiner(s): ABEBE, DANIEL DEMELASH
• Application Number:
  US08/876,601
• Time in Patent Office: 757 Days
• Field of Search: 704/222, 704/226, 704/251, 704/233, 704/256, 704/231
• US Class Current: 704/231
• CPC Class Codes: G10L 2019/0001 Codebooks
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

In a computerized method for processing speech signals, first vectors representing clean speech signals are stored in a vector codebook. Second vectors are determined from dirty speech signals. Noise and distortion parameters are estimated from the second vectors. Third vectors are predicated, based on estimated noise and distortion parameters. The third vectors are used to correct the first vectors. The third vectors can then be applied to the second vectors to produce corrected vectors. The corrected vectors and the first vectors can be compared to identify first vectors which resemble the corrected vectors.

Claims

1. A computerized method for processing speech signals, comprising:

storing first vectors representing clean speech signals in a vector codebook;

determining second vectors from dirty speech signals;

estimating environmental parameters from the second vectors;

predicting third vectors based on the estimated environmental parameters to correct the first vectors;

applying the third vectors to the second vectors to produce corrected vectors; and

comparing the corrected vectors and the first vectors to identify first vectors which resemble the corrected vectors;

wherein said method further comprises one of the following two steps;

(1) using a search algorithm to determine a hypothesis sequence of phonemes of said first vectors that is statistically closest to a sequence of said corrected vectors, and (2) determining mean and covariance for predicted statistics of said dirty speech signals and measuring likelihood that an utterance was generated by a particular speaker based upon an expectation maximization process.

2. The method of claim 1 wherein the third vectors are stored in the vector codebook.

3. The method of claim 1 further comprising:

determining a distance between a particular corrected vector and a corresponding first vector, the distance representing a likelihood that the corresponding first vector resembles the particular corrected vector.

4. The method of claim 3 further comprising:

maximizing the likelihood that the particular corrected vector resembles the coresponding first vector.

5. The method of claim 3 wherein the likelihood that the corresponding first vector resembles the particular corrected vector is a posterior probability that a particular third vector is represented by the corresponding first vector.

6. The method of claim 1 wherein the comparing step uses a statistical comparison.

7. The method of claim 6 wherein the statistical comparison is based on a minimum mean square error.

8. The method of claim 1 wherein the first vectors represent phonemes of the clean speech, and the comparison step determines the content of the dirty speech to perform speech recognition.

9. The method of claim 1 wherein the first vectors represent models of clean speech of known speakers, and the comparison step determines the identity of an unknown speaker producing the dirty speech signals.

10. The method of claim 1 wherein the dirty speech signals are produced continuously.

11. The method of claim 1 wherein the third vectors are dynamically adapted as the environmental parameters alter the dirty speech signals over time.

12. The method of claim 1 wherein the environmental parameters characterize noise and distortion by the variables Q, H, and Σ

_n.