Speech coding and joint data/channel bias estimation using finite state vector quantizer derived from sequential constraints

• US 5,778,336 A
• Filed: 10/01/1996
• Issued: 07/07/1998
• Est. Priority Date: 10/01/1996
• Status: Expired due to Term

• Current Assignee: Nokia of America Corporation (Nokia Corporation)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Chou, Wu, Seshadri, Nambirajan
• Primary Examiner(s): Hudspeth, David R.
• Assistant Examiner(s): Smits, Talivaldis Ivars
• Application Number:
  US08/722,416
• Time in Patent Office: 644 Days
• Field of Search: 704/222, 704/230, 704/245
• US Class Current: 704/222
• CPC Class Codes: H03M 7/3082 Vector coding for television signals, see H04N19/94

Abstract

A joint data (features) and channel (bias) estimation framework for robust processing of speech received over a channel is described. A trellis encoded vector quantizer is used as a pre-processor to estimate the channel bias using blind maximum likelihood sequence estimation. Sequential constraint in the feature vector sequence of a speech signal is applied for the selection of the quantized signal constellation and for the decoding process in joint data and channel estimation. A two state trellis encoded vector quantizer is designed for signal bias removal applications.

Claims

1. A method for conditioning a speech signal to compensate said signal for unknown distortion components included therein, wherein said conditioning is related to a coding of said signal based on sequential constraints characterizing said signal, said method comprising the steps of:

selecting a set of models for general acoustic modeling of human speech;

decomposing said model set into a plurality of acoustic classes, said classes reflecting a general correspondence with said sequential constraints;

determining a set of centroids for clusters of training speech feature vectors included in each said decomposed acoustic class;

operating on said speech feature vector centroids in each said decomposed acoustic class with a quantizing means to determine at least one code word corresponding to each said decomposed acoustic class;

constructing a finite state vector quantizer based on said code words so determined;

using said finite state vector quantizer to decode speech feature vectors of said signal, thereby determining which portions of said signal correspond to each of said code words, said decoded speech feature vectors collectively comprising a minimum sequence distortion path in said finite state vector quantizer;

estimating a bias term in said signal along said minimum sequence distortion path; and

subtracting said estimated bias term from said signal.

2. The method for conditioning a speech signal of claim 1, wherein said quantizing means of said operating step is implemented as a vector quantizer.

3. The method for conditioning a speech signal of claim 1, wherein said finite state vector quantizer is realized as a trellis encoded vector quantizer.

4. The method for conditioning a speech signal of claim 1, wherein said plurality of acoustic classes comprise at least four such acoustic classes.

5. The method for conditioning a speech signal of claim 1, wherein said selected model set is of the class of Hidden Markov Models.

6. The method for conditioning a speech signal of claim 1, wherein an output of said subtracting step is provided to a speech processing application.

7. The method for conditioning a speech signal of claim 6, wherein said speech processing application is a speech recognizer.

8. The method for conditioning a speech signal of claim 6, wherein said speech processing application is a speech enhancer.

9. A method for providing a coding for a speech signal, wherein said coding embeds sequential constraints characterizing human speech comprising the steps of:

selecting a set of models for general acoustic modeling of human speech;

decomposing said model set into a plurality of acoustic classes, said classes reflecting a general correspondence with said sequential constraints;

determining a set of centroids for clusters of training speech feature vectors included in each said decomposed acoustic class; and

operating on said speech feature vector centroids in each said decomposed acoustic class with a quantizing means to determine at least one code word corresponding to each said decomposed acoustic class.

10. The method for providing a coding for a speech signal of claim 9, wherein said quantizing means of said operating step is implemented as a vector quantizer.

11. The method for providing a coding for a speech signal of claim 9, wherein said code words determined in said operating step form a code-word constellation for a finite state vector quantizer.

12. The method for providing a coding for a speech signal of claim 11, wherein said finite state vector quantizer is realized as a trellis encoded vector quantizer.

13. The method for providing a coding for a speech signal of claim 9, wherein said plurality of acoustic classes comprise at least four such acoustic classes.

14. The method for providing a coding for a speech signal of claim 9, wherein said selected model set is of the class of Hidden Markov Models.

15. A method for reducing the effect of an unknown signal bias in an input speech signal, wherein a set of code words which embed sequential constraints inherent in said speech signal is generated, and further wherein said code word set is incorporated in a finite state vector quantizer as a code constellation thereof, said method comprising the steps of:

using said finite state vector quantizer to decode speech feature vectors of said speech signal, thereby determining which portions of said speech signal correspond to each of said code words, said decoded speech feature vectors collectively comprising a minimum sequence distortion path in said finite state vector quantizer;

estimating a bias term in said speech signal along said minimum sequence distortion path; and

subtracting said estimated bias term from said speech signal.

16. The method for reducing the effect of an unknown signal bias of claim 15, wherein said finite state vector quantizer is realized as a trellis encoded vector quantizer.

17. The method for reducing the effect of an unknown signal bias of claim 15, wherein said set of code words comprises at least four such code words.

18. The method for reducing the effect of an unknown signal bias of claim 15, wherein an output of said subtracting step is provided to a speech processing application.

19. The method for reducing the effect of an unknown signal bias of claim 18, wherein said speech processing application is a speech recognizer.

20. The method for reducing the effect of an unknown signal bias of claim 18, wherein said speech processing application is a speech enhancer.

21. A system for reducing the effect of an unknown signal bias in an input speech signal, wherein said system operates on a coding of said signal based on sequential constraints characterizing said signal, said system comprising:

means for decomposing a selected set of models describing acoustic events of human speech into a plurality of acoustic classes, said classes reflecting a general correspondence with said sequential constraints;

a first processing means for determining a set of centroids for clusters of training speech feature vectors included in each said decomposed acoustic class;

a second processing means operating on said speech feature vector centroids in each said decomposed acoustic class to implement a quantizing process to thereby determine at least one code word corresponding to each said decomposed acoustic class;

a means for constructing a finite state vector quantizer based on said code words so determined;

a third processing means for causing said finite state vector quantizer to decode speech feature vectors of said signal, thereby determining which portions of said signal correspond to each of said code words, said decoded speech feature vectors collectively comprising a minimum sequence distortion path in said finite state vector quantizer;

a fourth processing means for estimating a bias term in said signal along said minimum sequence distortion path; and

a fifth processing means for subtracting said estimated bias term from said signal;

and wherein one or more of said first through said fifth processing means may be combined into a common processing means.

22. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, wherein said quantizing process carried out by said second processing means is implemented as a vector quantization process.

23. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, wherein said finite state vector quantizer is realized as a trellis encoded vector quantizer.

24. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, wherein said plurality of acoustic classes established by said means for decomposing comprise at least four such acoustic classes.

25. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, wherein said selected model set is of the class of Hidden Markov Models.

26. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, further including a speech recognizer means operating on an output signal from said fifth processing means.

27. The system for reducing the effect of an unknown signal bias in an input speech signal of claim 21, further including a speech enhancement means operating on an output signal from said fifth processing means.

28. In a method for processing a speech signal wherein a bias term representing unknown distortion in said signal is estimated and then subtracted from said signal, the improvement therein comprising the steps of:

determining a set of code words from a set of acoustic models of human speech which embed sequential constraints inherent in human speech;

constructing a finite state vector quantizer wherein said code word set comprises a code constellation for said finite state vector quantizer;

applying said finite state vector quantizer for decoding said signal; and

estimating said bias term from data provided by said finite state vector quantizer decoding.