System and method for machine-mediated human-human conversation

• US 9,741,338 B2
• Filed: 12/09/2015
• Issued: 08/22/2017
• Est. Priority Date: 12/06/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Bangalore, Srinivas
• Primary Examiner(s): Godbold, Douglas
• Application Number:
  US14/963,479
• Publication Number:
  US 20160093296A1
• Time in Patent Office: 622 Days
• CPC Class Codes: G10L 15/075 supervised, i.e. under machine guidance
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 15/25 using position of the lips, movement of the lips or face analysis
• CPC Class Codes: G10L 17/22 Interactive procedures; Man-machine interfaces
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command
• CPC Class Codes: G10L 2015/228 of application context
• CPC Class Codes: G10L 25/84 for discriminating voice from noise

Abstract

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for processing speech. A system configured to practice the method monitors user utterances to generate a conversation context. Then the system receives a current user utterance independent of non-natural language input intended to trigger speech processing. The system compares the current user utterance to the conversation context to generate a context similarity score, and if the context similarity score is above a threshold, incorporates the current user utterance into the conversation context. If the context similarity score is below the threshold, the system discards the current user utterance. The system can compare the current user utterance to the conversation context based on an n-gram distribution, a perplexity score, and a perplexity threshold. Alternately, the system can use a task model to compare the current user utterance to the conversation context.

Claims

1. A method comprising:

generating a conversation context model based on user utterances and facial recognition data, wherein the conversation context model comprises a model of a speech dialog occurring between a speech dialog system and a speaker;

continuously comparing the speech dialog to the conversation context model, to yield a context similarity score;

modifying the context similarity score based on a head orientation of the speaker, to yield a modified context similarity score;

when the modified context similarity score is above a threshold, incorporating a current user utterance into the conversation context model for use in the speech dialog; and

when the modified context similarity score is one of equaling the threshold and below the threshold, suppressing the current user utterance such that the current user utterance is not incorporated into the conversation context model and the speech dialog produces speech as though the current user utterance is not in the conversation context model.

2. The method of claim 1, further comprising:

computing an n-gram distribution for the user utterances in the conversation context model;

computing a perplexity of the current user utterances based on the n-gram distribution;

when, based on a perplexity threshold, the current user utterance is a continuation of the conversation context model, incorporating the current user utterance into the conversation context model and updating the n-gram distribution based on the user utterance; and

when, based on the perplexity threshold, the current user utterance is not a continuation of the conversation context model, discarding the current user utterance.

3. The method of claim 1, wherein comparing the current user utterance to the conversation context model is based on a task model associated with a specific task.

4. The method of claim 3, wherein the task model indicates one of a conversation structure, a grammar, and a dictionary.

5. The method of claim 1, wherein the conversation context model is generated using a combination of speech recognition and a non-speech context source.

6. The method of claim 1, wherein user utterances are monitored without being triggered by a “

push-to-talk”

event.

7. The method of claim 6, wherein the “

push-to-talk”

event comprises explicit signaling to control a microphone.

8. The method of claim 1, wherein the conversation context model describes a human-human dialog.

9. The method of claim 1, wherein the conversation context model describes a human-machine dialog.

10. The method of claim 1, further comprising applying a noise suppression mechanism when monitoring the user utterances.

11. The method of claim 1, further comprising determining that the current user utterance is not intended to trigger the speech processing because of a deviation from the conversation context model which exceeds the threshold.

12. The method of claim 11, wherein the threshold is based on one of a user, a topic, the conversation context model, confidence scores, and background noise.

13. A system comprising:

a processor; and

a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising;

generating a conversation context model based on user utterances and facial recognition data, wherein the conversation context model comprises a model of a speech dialog occurring between a speech dialog system and a speaker;

continuously comparing the speech dialog to the conversation context model, to yield a context similarity score;

modifying the context similarity score based on a head orientation of the speaker, to yield a modified context similarity score;

when the modified context similarity score is above a threshold, incorporating a current user utterance into the conversation context model for use in the speech dialog; and

when the modified context similarity score is one of equaling the threshold and below the threshold, suppressing the current user utterance such that the current user utterance is not incorporated into the conversation context model and the speech dialog produces speech as though the current user utterance is not in the conversation context model.

14. The system of claim 13, the computer-readable storage medium having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

computing an n-gram distribution for the user utterances in the conversation context model;

computing a perplexity of the current user utterance based on the n-gram distribution;

when, based on a perplexity threshold, the current user utterance is a continuation of the conversation context model, incorporating the current user utterance into the conversation context model and updating the n-gram distribution based on the current user utterance; and

when, based on the perplexity threshold, the current user utterance is not a continuation of the conversation context model, discarding the current user utterance.

15. The system of claim 14, wherein comparing the current user utterance to the conversation context model is based on a task model associated with a specific task.

16. The system of claim 15, wherein the task model indicates one of a conversation structure, a grammar, and a dictionary.

17. The system of claim 14, wherein the conversation context model is generated using a combination of speech recognition and a non-speech context source.

18. The system of claim 14, wherein user utterances are monitored without being triggered by a “

push-to-talk”

event.

19. The system of claim 14, wherein the conversation context model describes a human-human dialog.

20. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:

generating a conversation context model based on user utterances and facial recognition data, wherein the conversation context model comprises a model of a speech dialog occurring between a speech dialog system and a speaker;

continuously comparing the speech dialog to the conversation context model, to yield a context similarity score;

modifying the context similarity score based on a head orientation of the speaker, to yield a modified context similarity score;

when the modified context similarity score is above a threshold, incorporating a current user utterance into the conversation context model for use in the speech dialog; and

when the modified context similarity score is one of equaling the threshold and below the threshold, suppressing the current user utterance such that the current user utterance is not incorporated into the conversation context model and the speech dialog produces speech as though the current user utterance is not in the conversation context model.