SPEECH INTERFACES

• US 20100057452A1
• Filed: 08/28/2008
• Published: 03/04/2010
• Est. Priority Date: 08/28/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Meeder, Brendan, Mukerjee, Kunal
• Application Number:
  US12/200,250
• Publication Number:
  US 20100057452A1
• Time in Patent Office: Days
• US Class Current: 704/232
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/16 using artificial neural networks

Abstract

The described implementations relate to speech interfaces and in some instances to speech pattern recognition techniques that enable speech interfaces. One system includes a feature pipeline configured to produce speech feature vectors from input speech. This system also includes a classifier pipeline configured to classify individual speech feature vectors utilizing multi-level classification.

Claims

1. A system, comprising:

a feature pipeline configured to produce speech feature vectors from speech; and

,

a classifier pipeline configured to classify individual speech feature vectors utilizing multi-level classification.

2. The system of claim 1, wherein the feature pipeline is configured to record a power level of the speech at multiple frequencies and to normalize the speech to a reference level for further processing.

3. The system of claim 2, wherein the feature pipeline is configured to produce the speech feature vectors from the speech utilizing a combination of dimensionality-reduced Mel coefficients and the power level.

4. The system of claim 1, wherein the classifier pipeline comprises a first coarse-level classifier configured to identify a probability that an individual speech feature vector matches one or more phoneme classes, wherein individual phoneme classes include one or more member phonemes.

5. The system of claim 4, wherein the classifier pipeline further comprises a second fine-level classifier configured to identify a probability that the individual speech feature vector matches individual member phonemes of an identified phoneme class.

6. The system of claim 1, wherein the classifier pipeline comprises a first multi-layer perceptron (MLP) configured to provide coarse level classification on the speech feature vectors and a second MLP configured to receive output from the first MLP and provide fine level classification.

7. The system of claim 1, wherein at least a portion of the classifier pipeline is stored in memory as nibbles and bytes.

8. The system of claim 1, wherein the classifier pipeline comprises a committee of multi-layer perceptrons (MLPs) configured to provide coarse level classification on the speech feature vectors and wherein some MLPs of the committee are trained to emphasize identifying some phoneme classes while other different MLPs of the committee are trained to emphasize identifying other different phoneme classes.

9. A computer-readable storage media having instructions stored thereon that when executed by a computing device cause the computing device to perform acts, comprising:

receiving speech;

identifying a probability that a segment of the speech matches one or more phoneme classes, where phoneme classes include one or more phonemes; and

,

determining a probability that the segment matches an individual phoneme of an identified phoneme class.

10. The computer-readable storage media of claim 9, wherein the receiving comprises processing the speech to generate corresponding de-correlated data and wherein the identifying comprises identifying a probability that the de-correlated data matches one or two of the one or more phoneme classes.

11. The computer-readable storage media of claim 9, wherein the identifying further comprises comparing the probability for individual phoneme classes to a threshold and in an instance where the probability for an individual phoneme class exceeds the threshold then recording a symbol that indicates that the segment matches the individual phoneme class.

12. The computer-readable storage media of claim 9, wherein the identifying further comprises comparing the probability for individual phoneme classes to a first threshold and in an instance where the probability for any individual phoneme class is less than the first threshold, but where combined probabilities of two individual phoneme classes exceeds a second threshold then recording that the segment matches either of the two individual phoneme classes.

13. The computer-readable storage media of claim 9, wherein the identifying further comprises comparing the probability for individual phoneme classes to a first threshold and in an instance where the probability for any individual phoneme class is less than the first threshold and a combined probabilities of any two individual phoneme classes does not exceed a second threshold then recording a wildcard symbol for the segment that indicates that the segment is unknown.

14. The computer-readable storage media of claim 9, wherein the determining indicates a match where the probability for an individual phoneme exceeds a threshold.

15. The computer-readable storage media of claim 9, further comprising in an instance where a duration of the segment exceeds a minimum value, recording a symbol that corresponds to the identified phoneme class and another symbol that corresponds to the determined phoneme.

16. A method, comprising:

receiving probabilities that speech corresponds to one or more phoneme classes; and

,

based at least in part on the probabilities, assigning a segment of the speech one of;

a single phoneme-based speech descriptor symbol, two alternative phoneme-based speech descriptor symbols, and a wildcard symbol.

17. The method of claim 16, wherein the assigning is based upon a graphical representation of probabilities of the speech matching individual phoneme classes over time.

18. The method of claim 16, wherein the assigning is performed where a duration of the segment is at least about 100 milliseconds.

19. The method of claim 16, further comprising recording the assigned symbol or symbols and a duration of the segment.

20. The method of claim 16, further comprising in an instance where a duration of the segment is below a predefined value then not recording the assigned symbol.