A METHOD AND SYSTEM FOR SPEECH CLASSIFICATION

• US 20080033720A1
• Filed: 08/04/2006
• Published: 02/07/2008
• Est. Priority Date: 08/04/2006
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Faruquie, Tanveer Afzal, Kankar, Pankaj
• Application Number:
  US11/462,396
• Publication Number:
  US 20080033720A1
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding

Abstract

The present invention deals with a method and system for classifying at least one speech-user-utterance in a speech classification system to one of a plurality of pre-defined class types. The method comprises transcribing automatically the at least one speech-user-utterance to obtain at least one automatic-transcribed-text and estimating an estimated-transcribed-text corresponding to the at least one automatic-transcribed-text. The method further comprises classifying the at least one speech-user-utterance based on the estimated-transcribed-text. The estimated-transcribed-text is estimated based on at least one statistical model.

Claims

1. A method for classifying at least one speech-user-utterance to one of a plurality of pre-defined class types, the method comprising the steps of:

a. transcribing the at least one speech-user-utterance to obtain at least one automatic-transcribed-text, wherein the transcribing is done automatically, wherein the at least one automatic-transcribed-text corresponds to the at least one speech-user-utterance;

b. estimating an estimated-transcribed-text corresponding to the at least one automatic-transcribed-text, the estimated-transcribed-text being estimated based on at least one statistical model; and

c. classifying the at least one speech-user-utterance based on the estimated-transcribed-text.

2. The method of claim 1, wherein the transcribing step comprises transcribing the at least one speech-user-utterance using an automatic speech recognizer.

3. The method of claim 1, wherein the at least one statistical model is based on one of a direct modeling technique and an indirect modeling technique.

4. The method of claim 3, wherein the direct modeling technique is a maximum entropy modeling technique.

5. The method of claim 3, wherein the indirect modeling technique is a statistical machine translation (SMT) system based on a source channel technique and an n-gram replacement technique.

6. The method of claim 1, wherein the step of estimating comprises:

a. calculating a probability for a human-transcribed-text of being a transcription of the at least one speech-user-utterance based on the at least one automatic-transcribed-text, the probability being calculated for each of a plurality of human-transcribed-texts, the probability being calculated based on a statistical model; and

b. selecting a human-transcribed-text with a highest probability, the human-transcribed-text being one of the plurality of human-transcribed-texts.

7. A method for managing at least one speech-user-utterance in a speech classification system, the at least one speech-user-utterance being classified to one of a plurality of pre-defined class types, the method comprising:

a. training the speech classification system, the training step comprising;

i. converting at least one predefined-speech-user-utterance to a human-transcribed-text, the converting being done manually;

ii. transcribing the at least one predefined-speech-user-utterance to obtain a first set of automatic-transcribed-text, wherein the at least one predefined-speech-user-utterance is transcribed automatically using an automated speech recognizer; and

iii. modeling a relationship between the human-transcribed-text and the first set of automatic-transcribed-text, wherein a probabilistic model is used to model the relationship.

b. classifying the at least one speech-user-utterance, the classifying step comprising;

i. transcribing the at least one speech-user-utterance to obtain a second set of automatic-transcribed-text corresponding to the at least one speech-user-utterance, the transcribing being done automatically;

ii. estimating an estimated-transcribed-text corresponding to the second set of automatic-transcribed-text based on the relationship modeled in the training step; and

iii. classifying the at least one speech-user-utterance to one of the plurality of pre-defined class types based on the corresponding estimated-transcribed-text.

8. The method of claim 7, wherein the speech classification system is trained offline.

9. The method of claim 7, wherein the at least one speech-user-utterance is classified in real-time.

10. The method of claim 7, wherein the human-transcribed-text is pre-processed to obtain a processed-human-transcribed-text, wherein the human-transcribed-text is pre-processed based on at least one of a part-of-speech (POS) tagging, a morphing, a stemming, a tokenization and a parsing.

11. The method of claim 7, wherein the estimated-transcribed-text is estimated based on a relationship, the relationship being modeled between the processed-human-transcribed-text and the first set of automatic-transcribed-text.

12. The method of claim 7, wherein the speech classification system routes a voice call to a pre-defined class type, the pre-defined class type corresponding to an appropriate department.

13. A system for managing at least one speech-user-utterance in a speech classification system, the at least one speech-user-utterance being classified to one of a plurality of pre-defined class types, the system comprising:

a. a training module, the training module comprises;

i. a first automatic-transcribing unit, the first automatic-transcribing unit transcribing at least one predefined-speech-user-utterance to obtain a first set of automatic-transcribed-text; and

ii. a modeling unit, the modeling unit modeling a relationship between a human-transcribed-text and each automatic-transcribed-text in the first set of automatic-transcribed-text, wherein a probabilistic model is used to model the relationship, wherein the at least one predefined-speech-user-utterance is transformed manually to obtain the human-transcribed-text.

b. a classifying module, the classifying module comprises;

i. a second automatic-transcribing unit, the second automatic-transcribing unit transcribing the at least one speech-user-utterance to obtain a second set of automatic-transcribed-text;

ii. an estimating unit, the estimating unit estimating an estimated-transcribed-text corresponding to the second set of automatic-transcribed-text based on the relationship modeled by the modeling unit; and

iii. a classifier unit, the classifier unit classifying the at least one speech-user-utterance to one of the plurality of pre-defined class types based on the corresponding estimated-transcribed-text.

14. The system of claim 13, wherein the estimating unit comprises:

a. a calculating unit, the calculating unit calculating a probability for a human-transcribed-text of being a transcription of the at least one speech-user-utterance based on the second set of automatic-transcribed-text, the probability being calculated for each of a plurality of human-transcribed-texts, the probability being calculated based on a statistical model; and

b. a selecting unit, the selecting unit selecting a human-transcribed-text with a highest probability.

15. The system of claim 13, wherein the human-transcribed-text is pre-processed to obtain a processed-human-transcribed-text, wherein the human-transcribed-text is pre-processed based on at least one of a part-of-speech (POS) tagging, a morphing, a stemming, a tokenization and a parsing.

16. The system of claim 15, wherein the estimating unit estimates the estimated-transcribed-text based on a relationship, the relationship being modeled between the processed-human-transcribed-text and the second set of automatic-transcribed-text.