ADAPTIVE END-OF-UTTERANCE TIMEOUT FOR REAL-TIME SPEECH RECOGNITION
Real-time speech recognition systems extend an end-of-utterance timeout period in response to the presence of a disfluency at the end of speech, and by so doing avoid cutting off speakers mid-sentence. Approaches to detecting disfluencies include the application of disfluency n-gram language models, acoustic models, prosody models, and phrase spotting. Explicit pause phrases can also be detected to extend sentence parsing until relevant semantic information is gathered from the speaker or another voice. Disfluency models can be trained such as by searching by successive deletion of tokens, phonemes, or acoustic segments to convert sentences that cannot be parsed into ones that can. Disfluency-based timeout adaptation is applicable to safety-critical systems.
1 . A method of adapting an end-of-utterance timeout in a real-time speech recognition system, the method comprising:
detecting, on a real-time basis, periods of voice activity and no voice activity in a received audio sequence;
computing, on a real-time basis, a disfluency score from the audio sequence;
adapting, during receiving of the audio sequence, an end-of-utterance timeout as a function of the disfluency score to prevent an improper timeout that disrupts receiving a complete sentence in the audio sequence; and
signaling an end-of-utterance event in response to detecting a period of no voice activity exceeding the adapted end-of-utterance timeout.
2 . The method of claim 1 further comprising computing, on a real-time basis, a transcription from the received audio sequence, wherein computing the disfluency score is by applying a language model to the transcription.
3 . The method of claim 1 further comprising:
computing, on a real-time basis, an acoustic disfluency feature from the audio sequence; and
adapting, during receiving of audio sequence, the end-of-utterance timeout as a function of the acoustic disfluency feature.
4 . The method of claim 1 further comprising:
computing, on a real-time basis, a prosodic disfluency feature from the audio sequence; and
adapting, during receiving of audio sequence, the end-of-utterance timeout as a function of the prosodic disfluency feature.
5 . The method of claim 1 wherein computing the disfluency score is by use of a phrase spotter to detect a disfluency phrase.
6 . A non-transitory computer-readable medium storing code that, if executed by one or more computer processors would cause the one or more computer processors to:
detect, on a real-time basis, periods of voice activity and no voice activity in a received audio sequence;
compute, on a real-time basis, a disfluency score from the audio sequence;
adapt, during receiving of the audio sequence, an end-of-utterance timeout as a function of the disfluency score to prevent an improper timeout that disrupts receiving a complete sentence in the audio sequence; and
signal an end-of-utterance event in response to detecting a period of no voice activity exceeding the adapted end-of-utterance timeout.
7 . The non-transitory computer-readable medium of claim 6 that would further cause the one or more computer processors to compute, on a real-time basis, a transcription from the received audio sequence, wherein the computed disfluency score is by applying a language model to the transcription.
8 . The non-transitory computer-readable medium of claim 7 wherein the language model is a classifier.
9 . The non-transitory computer-readable medium of claim 7 wherein the language model is a neural network.
10 . The non-transitory computer-readable medium of claim 6 that would further cause the one or more computer processors to:
compute, on a real-time basis, an acoustic disfluency feature from the audio sequence; and
adapt, during receiving of audio sequence, the end-of-utterance timeout as a function of the acoustic disfluency feature.
11 . The non-transitory computer-readable medium of claim 6 that would further cause the one or more computer processors to:
compute, on a real-time basis, a prosodic disfluency feature from the audio sequence; and
adapt, during receiving of audio sequence, the end-of-utterance timeout as a function of the prosodic disfluency feature.
12 . The non-transitory computer-readable medium of claim 6 wherein computing the disfluency score is by use of a phrase spotter for a disfluency phrase.
13 . A method of parsing sentences with disfluent interruptions, the method comprising:
parsing a sentence from a sequence of received speech;
detecting, in the sequence of received speech, a pause phrase;
after detecting the pause phrase, parsing semantic information from the sequence of received speech to detect semantic information appropriate to continuing the parsing; and
in response to detecting the semantic information, continue parsing the the sentence.
14 . The method of claim 13 wherein the received speech before the pause phrase is from a first speaker and the semantic information is from a second speaker.
15 . The method of claim 13 further comprising:
performing voice characterization on the received speech; and
wherein detecting semantic information is conditional on the voice expressing the semantic information matching the voice in the received speech before the pause phrase.
16 . A method of disfluency-adaptive real-time speech recognition, the method comprising:
detecting a disfluency in received audio that includes periods of speech activity and periods of no voice activity;
adapting a timeout, based on the detection of the disfluency, to prevent an improper end-of-utterance that disrupts receiving a complete sentence; and
signaling an end-of-utterance event in response to detection of no voice activity exceeding the adapted timeout.
17 . The method of claim 16 wherein the step of adapting includes detecting an acoustic disfluency feature.
18 . The method of claim 16 wherein the step of adapting includes detecting a prosodic disfluency feature.
19 . A disfluency-adaptive real-time speech recognition system comprising:
means to detect a disfluency in received audio that includes periods of speech activity and periods of no voice activity; and
means to signal an end-of-utterance event in response to detection of no voice activity exceeding a timeout,
wherein the timeout is adapted based on the detection of a disfluency to prevent an improper end-of-utterance event that disrupts receiving a complete sentence.
20 . The system of claim 19 wherein the real-time speech recognition system is an automobile control module.
21 . The system of claim 19 wherein the real-time speech recognition system is safety critical and the detection of a disfluency is by computing a disfluency score and the disfluency score affects operational decision making.
22 . A method of training a disfluency model, the method comprising:
performing a multiplicity of token deletion searches on transcriptions that cannot be parsed to identify a token within the transcriptions that, if deleted, turns the transcriptions that cannot be parsed into transcriptions that can be parsed; and
training a statistical language model using the deleted token based on its contexts within the multiplicity of transcriptions that cannot be parsed,
wherein the statistical language model is useful to infer disfluencies in transcriptions.