Abstract: According to some aspects, a system for detecting a designated wake-up word is provided, the system comprising a plurality of microphones to detect acoustic information from a physical space having a plurality of acoustic zones, at least one processor configured to receive a first acoustic signal representing the acoustic information received by the plurality of microphones, process the first acoustic signal to identify content of the first acoustic signal originating from each of the plurality of acoustic zones, provide a plurality of second acoustic signals, each of the plurality of second acoustic signals substantially corresponding to the content identified as originating from a respective one of the plurality of acoustic zones, and performing automatic speech recognition on each of the plurality of second acoustic signals to determine whether the designated wake-up word was spoken.

Abstract: Techniques are provided for dynamic adaptation of language understanding systems to acoustic environments. A methodology implementing the techniques according to an embodiment includes generating a trigger in response to recognition of a wake-on-voice key-phrase in or prior to an audio stream. The trigger serves to switch processing modes from an adaptation mode to a query recognition mode. The method further includes performing automatic speech recognition on the audio stream during the query recognition mode, to recognize an in-domain query. The method further includes applying both a static language understanding classifier and a dynamic language understanding classifier to the recognized in-domain query. The static language understanding classifier employs a static semantic model and the dynamic language understanding classifier employs a dynamic semantic model.

Abstract: An example apparatus for detecting intent in voiced audio includes a receiver to receive one or more word sequence hypotheses related to a voiced audio and a dynamic vocabulary. The apparatus also includes a natural language understander (NLU) to detect an intent and recognize a property related to the intent based on the word sequence hypothesis and the dynamic vocabulary. The apparatus further includes a transmitter to transmit the detected intent and recognized associated property to an application.

Abstract: An example apparatus for routing audio streams includes an audio receiver to receive audio from a microphone. The apparatus also includes a classifier to semantically generate a result set based on the audio. The apparatus further includes a scheduler to select a spoken language understanding (SLU) engine based on the result set. The apparatus includes a router to route the audio to the selected SLU engine.

Abstract: Techniques are provided for wake-on-voice (WOV) key-phrase enrollment. A methodology implementing the techniques according to an embodiment includes generating a WOV key-phrase model based on identification of the sequence of sub-phonetic units of a user-provided key-phrase. The WOV key-phrase model is employed by a WOV processor for detection of the user spoken key-phrase and triggering operation of an automatic speech recognition (ASR) processor in response to the detection. The method further includes updating an ASR language model based on the user-provided key-phrase. The update includes one of embedding the WOV key-phrase model into the ASR language model, converting sub-phonetic units of the WOV key-phrase model and embedding the converted WOV key-phrase model into the ASR language model, or generating an ASR key-phrase model by applying a phoneme-syllable based statistical language model to the user-provided key-phrase and embedding the generated ASR key-phrase model into the ASR language model.

Abstract: Techniques are provided for reducing the latency of automatic speech recognition using hypothesis score trend analysis. A methodology implementing the techniques according to an embodiment includes generating complete-phrase hypotheses and partial-phrase hypotheses, along with associated likelihood scores, based on a segment of speech. The method also includes selecting the complete-phrase hypothesis associated with the highest of the complete-phrase hypotheses likelihood scores, and selecting the partial-phrase hypothesis associated with the highest of the partial-phrase hypotheses likelihood scores. The method further includes calculating a relative likelihood score based on a ratio of the likelihood score associated with the selected complete-phrase hypothesis to the likelihood score associated with the selected partial-phrase hypothesis.

Abstract: An example apparatus for recognizing speech includes an audio receiver to receive a stream of audio. The apparatus also includes a key phrase detector to detect a key phrase in the stream of audio. The apparatus further includes a model adapter to dynamically adapt a model based on the detected key phrase. The apparatus also includes a query recognizer to detect a voice query following the key phrase in a stream of audio via the adapted model.

Abstract: An example apparatus for concealing phrases in audio includes a receiver to receive a detected phrase via a network. The detected phrase is based on audio captured near a source of an audio stream. The apparatus also includes a speech recognizer to generate a trigger in response to detecting that a section of the audio stream contains a confirmed phrase. The apparatus further includes a phrase concealer to conceal the section of the audio stream in response to the trigger.

Abstract: A method or associated system for motion adaptive speech processing includes dynamically estimating a motion profile that is representative of a user's motion based on data from one or more resources, such as sensors and non-speech resources, associated with the user. The method includes effecting processing of a speech signal received from the user, for example, while the user is in motion, the processing taking into account the estimated motion profile to produce an interpretation of the speech signal. Dynamically estimating the motion profile can include computing a motion weight vector using the data from the one or more resources associated with the user, and can further include interpolating a plurality of models using the motion weight vector to generate a motion adaptive model. The motion adaptive model can be used to enhance voice destination entry for the user and re-used for other users who do not provide motion profiles.

Abstract: A method for context-aware query recognition in an electronic device includes receiving user speech from an input device. A speech signal is generated from the user speech. It is determined if the speech signal includes an action to be performed and if the electronic device is the intended recipient of the user speech. If the recognized speech signal include the action and the intended recipient of the user speech is the electronic device, a command is generated for the electronic device to perform the action.

Abstract: According to some aspects, a system for detecting a designated wake-up word is provided, the system comprising a plurality of microphones to detect acoustic information from a physical space having a plurality of acoustic zones, at least one processor configured to receive a first acoustic signal representing the acoustic information received by the plurality of microphones, process the first acoustic signal to identify content of the first acoustic signal originating from each of the plurality of acoustic zones, provide a plurality of second acoustic signals, each of the plurality of second acoustic signals substantially corresponding to the content identified as originating from a respective one of the plurality of acoustic zones, and performing automatic speech recognition on each of the plurality of second acoustic signals to determine whether the designated wake-up word was spoken.

Abstract: Techniques are provided for reducing the latency of automatic speech recognition using hypothesis score trend analysis. A methodology implementing the techniques according to an embodiment includes generating complete-phrase hypotheses and partial-phrase hypotheses, along with associated likelihood scores, based on a segment of speech. The method also includes selecting the complete-phrase hypothesis associated with the highest of the complete-phrase hypotheses likelihood scores, and selecting the partial-phrase hypothesis associated with the highest of the partial-phrase hypotheses likelihood scores. The method further includes calculating a relative likelihood score based on a ratio of the likelihood score associated with the selected complete-phrase hypothesis to the likelihood score associated with the selected partial-phrase hypothesis.

Abstract: Techniques are provided for wake-on-voice (WOV) key-phrase enrollment. A methodology implementing the techniques according to an embodiment includes generating a WOV key-phrase model based on identification of the sequence of sub-phonetic units of a user-provided key-phrase. The WOV key-phrase model is employed by a WOV processor for detection of the user spoken key-phrase and triggering operation of an automatic speech recognition (ASR) processor in response to the detection. The method further includes updating an ASR language model based on the user-provided key-phrase. The update includes one of embedding the WOV key-phrase model into the ASR language model, converting sub-phonetic units of the WOV key-phrase model and embedding the converted WOV key-phrase model into the ASR language model, or generating an ASR key-phrase model by applying a phoneme-syllable based statistical language model to the user-provided key-phrase and embedding the generated ASR key-phrase model into the ASR language model.

Abstract: Techniques are provided for dynamic adaptation of language understanding systems to acoustic environments. A methodology implementing the techniques according to an embodiment includes generating a trigger in response to recognition of a wake-on-voice key-phrase in or prior to an audio stream. The trigger serves to switch processing modes from an adaptation mode to a query recognition mode. The method further includes performing automatic speech recognition on the audio stream during the query recognition mode, to recognize an in-domain query. The method further includes applying both a static language understanding classifier and a dynamic language understanding classifier to the recognized in-domain query. The static language understanding classifier employs a static semantic model and the dynamic language understanding classifier employs a dynamic semantic model.

Abstract: An example apparatus for routing audio streams includes an audio receiver to receive audio from a microphone. The apparatus also includes a classifier to semantically generate a result set based on the audio. The apparatus further includes a scheduler to select a spoken language understanding (SLU) engine based on the result set. The apparatus includes a router to route the audio to the selected SLU engine.

Abstract: An example apparatus for detecting intent in voiced audio includes a receiver to receive one or more word sequence hypotheses related to a voiced audio and a dynamic vocabulary. The apparatus also includes a natural language understander (NLU) to detect an intent and recognize a property related to the intent based on the word sequence hypothesis and the dynamic vocabulary. The apparatus further includes a transmitter to transmit the detected intent and recognized associated property to an application.

Abstract: Systems and methods for processing results from plural speech services are described. A method includes receiving speech service results from plural speech services and service specifications corresponding to the speech service results. The results are at least one data structure representing information according to functionality of the speech services. The service specifications describe the data structure and its interpretation for each speech service. The speech service results are encoded into a unified conceptual knowledge representation of the results based on the service specification. The unified conceptual knowledge representation is provided to an application module. A method includes assessing speech service results received asynchronously from plural speech services to determine, based on a reliability measure, whether there is a reliable result among the speech service results received.

Abstract: Techniques are provided for language identification performed in conjunction with wake-on-voice keyword detection. A methodology implementing the techniques according to an embodiment includes applying phrase models to a user-spoken keyword. Each of the phrase models is configured to detect the keyword in a selected language and to generate a probability associated with the detection. The method further includes scoring the probabilities associated with the keyword detection in each of the languages, and identifying the language of the keyword based on the scoring. Automatic speech recognition and spoken language understanding systems may then be configured or selected to process further speech from the user in the identified language. In some embodiments, the phrase models are generated, in an offline process, based on provided grapheme sequences representing the keyword in the language associated with the phrase model.

Abstract: Techniques are provided for rejecting out-of-domain (OD) queries in a language understanding system. A methodology implementing the techniques according to an embodiment includes generating a plurality of in-domain (ID) utterances based on variations of provided ID sentences, and generating a plurality of OD utterances based on variations of provided OD sentences. The method may further include training an ID language model based on the generated ID utterances and training an OD language model based on the generated OD utterances. The ID language model is configured to generate an ID dataset based on calculated probabilities associated with the generated ID utterances. The OD language model is configured to generate an OD dataset based on calculated probabilities associated with the generated OD utterances. The method further includes training a classifier to detect OD queries from a plurality of received queries, the training based on the ID dataset and the OD dataset.

Abstract: A method for context-aware query recognition in an electronic device includes receiving user speech from an input device. A speech signal is generated from the user speech. It is determined if the speech signal includes an action to be performed and if the electronic device is the intended recipient of the user speech. If the recognized speech signal include the action and the intended recipient of the user speech is the electronic device, a command is generated for the electronic device to perform the action.