Prosody based endpoint detection

• US 6,873,953 B1
• Filed: 05/22/2000
• Issued: 03/29/2005
• Est. Priority Date: 05/22/2000
• Status: Expired due to Fees

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Lennig, Matthew
• Primary Examiner(s): Azad, Abul K.
• Application Number:
  US09/576,116
• Time in Patent Office: 1,772 Days
• Field of Search: 704/251, 704/252, 704/253, 704/254, 704/247, 704/248, 704/249, 704/250, 704/207, 704/208, 704/209, 704/210, 704/218, 704/219, 704/214, 704/215, 704/226, 704/231, 704/256, 704/261, 704/267, 704/268
• US Class Current: 704/253
• CPC Class Codes: G10L 25/87 Detection of discrete points within a voice signal

Abstract

A method and apparatus are provided for performing prosody based endpoint detection of speech in a speech recognition system. Input speech represents an utterance, which has an intonation pattern. An end-of-utterance condition is identified based on prosodic parameters of the utterance, such as the intonation pattern and the duration of the final syllable of the utterance, as well as non-prosodic parameters, such as the log energy of the speech.

Claims

1. A method of operating an endpoint detector for speech recognition, the method comprising:

inputting speech representing an utterance;

determining that a value of the speech has dropped below a threshold value;

computing an intonation of the utterance;

referencing the intonation of the utterance against an intonation model to determine a first end-of-utterance probability;

determining a period of time that has elapsed since the value of the speech dropped below the threshold value;

referencing the period of time against an elapsed time model to determine a second end-of-utterance probability;

computing an overall end-of-utterance probability as a function of the first and second end-of-utterance probabilities; and

determining whether an end-of-utterance has occurred based on the overall end-of-utterance probability.

2. A method as recited in claim 1, wherein said computing an intonation of the utterance comprises computing an intonation of the utterance by determining the fundamental frequency of the utterance as a function of time.

3. A method as recited in claim 2, further comprising:

determining a duration of a final syllable of the utterance; and

,

referencing the duration of the final syllable against a syllable duration model to determine a third end-of-utterance probability;

wherein said computing an overall end-of-utterance probability comprises computing the overall end-of-utterance probability as a function of the first, second, and third end-of-utterance probabilities.

4. A method of operating an endpoint detector for speech recognition, the method comprising:

inputting speech representing an utterance;

computing an intonation of the utterance;

referencing the intonation of the utterance against an intonation model to determine a first end-of-utterance probability;

determining a duration of a final syllable of the utterance;

referencing the duration of the final syllable against a syllable duration model to determine a second end-of-utterance probability;

computing an overall end-of-utterance probability as a function of the first and second end-of-utterance probabilities; and

determining whether an end-of-utterance has occurred based on the overall end-of-utterance probability.

5. A method as recited in claim 4, wherein said computing an intonation of the utterance comprises computing an intonation of the utterance by determining the fundamental frequency of the utterance as a function of time.

6. A method as recited in claim 4, further comprising:

determining that a value of the speech has dropped below a threshold value;

determining a period of time that has elapsed since the value of the speech dropped below the threshold value; and

referencing the period of time against an elapsed time model to determine a second end-of-utterance probability;

wherein said computing an overall end-of-utterance probability comprises computing the overall end-of-utterance probability as a function of the first, second, and third end-of-utterance probabilities.

7. A method of operating an endpoint detector for speech recognition, the method comprising:

inputting speech representing an utterance, the utterance having a time-varying fundamental frequency;

determining that a value of the speech has drooped below a threshold value;

computing an intonation of the utterance by determining the fundamental frequency of the utterance as a function of time;

referencing the intonation of the utterance against an intonation model to determine a first end-of-utterance probability;

determining a period of time that has elapsed since a value of the speech dropped below the threshold value;

referencing the period of time against an elapsed time model to determine a second end-of-utterance probability;

determining a duration of a final syllable of the utterance;

referencing the duration of the final syllable against a syllable duration model to determine a third end-of-utterance probability;

computing an overall end-of-utterance probability as a function of the first, second, and third end-of-utterance probabilities; and

determining whether an end-of-utterance has occurred by comparing the overall end-of-utterance probability to a threshold probability.

8. An apparatus for performing endpoint detection comprising:

means for inputting speech representing an utterance, the utterance having a time-varying fundamental frequency;

means for determining that a value of the speech has dropped below a threshold value;

means for computing an intonation of the utterance by determining the fundamental frequency of the utterance as a function of time;

means for referencing the intonation of the utterance against an intonation model to determine a first end-of-utterance probability;

means for determining a period of time that has elapsed since the speech dropped below the threshold value;

means for referencing the period of time against an elapsed time model to determine a second end-of-utterance probability;

means for computing the duration of the final syllable of the utterance against a syllable duration model to determine a third end-of-utterance probability;

means for determining an overall end-of-utterance probability as a function of the first, second, and third end-of-utterance probabilities; and

means for determining whether an end-of-utterance has occurred by comparing the overall end-of-utterance probability to a threshold probability.