Call classifier using automatic speech recognition to separately process speech and tones

• US 20030086541A1
• Filed: 10/23/2001
• Published: 05/08/2003
• Est. Priority Date: 10/23/2001
• Status: Abandoned Application

• Current Assignee: Danny M. Wages, David Joseph Skiba, Michael Kenneth Brown, Norman C. Chan, Sharmistha Sarkar Das
• Original Assignee: Danny M. Wages, David Joseph Skiba, Michael Kenneth Brown, Norman C. Chan, Sharmistha Sarkar Das
• Inventors: Brown, Michael Kenneth, Skiba, David Joseph, Das, Sharmistha Sarkar, Chan, Norman C., Wages, Danny M.
• Application Number:
  US10/037,588
• Publication Number:
  US 20030086541A1
• Time in Patent Office: Days
• US Class Current: 379/88.01
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: H04M 2201/40 using speech recognition
• CPC Class Codes: H04M 2203/2066 Call type detection of indication, e.g. voice or fax, mobile of fixed, PSTN or IP
• CPC Class Codes: H04M 3/02 Calling substations, e.g. by ringing selective calling H04Q
• CPC Class Codes: H04M 3/4228 in networks

Abstract

Classifying a call to a called destination endpoint by a call classifier. The call classifier is responsive to information received from the called destination endpoint to perform the call classification.

Claims

1. A method for doing call classification on a call to a destination endpoint, comprising the steps of:

receiving audio information from the destination endpoint;

analyzing using automatic speech recognition the received audio information for a first type of classification;

analyzing using automatic speech recognition the received audio information for a second type of classification; and

determining a call classification for the destination endpoint in response to the analysis of the first type of classification and the analysis of the second type of classification.

2. The method of claim 1 wherein the analysis for the first type of classification is analyzing the audio information for words.

3. The method of claim 2 wherein the analyzed words are formed as phrases.

4. The method of claim 2 wherein the analysis for the second type of classification is analyzing the audio information for tones.

5. The method of claim 4 wherein the step of receiving audio information further comprises detecting speech or tones in the audio information.

6. The method of claim 5 wherein the step of analyzing for the first type of classification is responsive to the detection of speech in the audio information to enable the step of executing a Hidden Markov Model to determine the presence of words in the audio information.

7. The method of claim 6 wherein the step of executing comprises the step of using a grammar for speech.

8. The method of claim 6 wherein the step of analyzing for the second type of classification is responsive to the detection of tone in the audio information to enable the step of executing a Hidden Markov Model to determine the presence of tones in the audio information.

9. The method of claim 8 wherein the step of executing comprises the step of using a grammar for tones.

10. The method of claim 8 wherein the step of determining comprises the step of executing an inference engine.

11. A method for doing call classification on a call to a destination endpoint, comprising the steps of:

receiving audio information from the destination endpoint;

detecting speech or tones in received audio information.

analyzing using automatic speech recognition the received audio information for words in response to the detection of speech;

analyzing using automatic speech recognition the received audio information for tones in response to the detection of tones; and

determining a call classification for the destination endpoint in response to the analysis of words or the analysis of tones.

12. The method of claim 11 wherein the step of analyzing for speech comprises the step of executing a Hidden Markov Model to determine the presence of words in the audio information.

13. The method of claim 12 wherein the step of executing comprises the step of using a grammar for speech.

14. The method of claim 12 wherein the step of analyzing for tones comprises the step of executing a Hidden Markov Model to determine the presence of tones in the audio information.

15. The method of claim 14 wherein the step of executing comprises the step of using a grammar for tones.

16. The method of claim 15 wherein the step of determining comprises the step of executing an inference engine.

17. A method for doing call classification by a automatic speech recognition unit on a call to a destination endpoint, comprising the steps of:

receiving audio information from the destination endpoint by the automatic speech recognition unit;

analyzing using automatic speech recognition the received audio information for a first type of classification by the automatic speech recognition unit;

analyzing using automatic speech recognition the received audio information for a second type of classification automatic speech recognition unit; and

determining a call classification for the destination endpoint in response to the analysis of the first type of classification and the analysis of the second type of classification by the automatic speech recognition unit.

18. The method of claim 17 wherein the analysis for the first type of classification is analyzing the audio information for words.

19. The method of claim 18 wherein the analyzed words are formed as phrases.

20. The method of claim 18 wherein the analysis for the second type of classification is analyzing the audio information for tones.

21. The method of claim 20 wherein the step of receiving audio information further comprises detecting speech or tones in the audio information.

22. The method of claim 21 wherein the step of analyzing for the first type of classification is responsive to the detection of speech in the audio information to enable the step of executing a Hidden Markov Model to determine the presence of words in the audio information.

23. The method of claim 22 wherein the step of executing comprises the step of using a grammar for speech.

24. The method of claim 22 wherein the step of analyzing for the second type of classification is responsive to the detection of tone in the audio information to enable the step of executing a Hidden Markov Model to determine the presence of tones in the audio information.

25. The method of claim 24 wherein the step of executing comprises the step of using a grammar for tones.

26. The method of claim 24 wherein the step of determining comprises the step of executing an inference engine.

27. A call classifier for determining the call classification of a called destination endpoint, comprising:

an automatic speech recognizer for detecting first characteristics in audio information received from the called destination endpoint;

the automatic speech recognizer further detecting second characteristics in the audio information received from the called destination endpoint; and

inference engine for classifying the call in response to the automatic speech recognizer.

28. The call classifier of claim 27 wherein the first characteristics are words.

29. The call classifier of claim 28 wherein the words are formed into phrases.

30. The call classifier of claim 28 wherein the second characteristics are tones.

31. The call classifier of claim 30 wherein the automatic speech recognizer is executing a Hidden Markov Model.