According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech endpointing are described. In one aspect, a method includes the action of accessing voice query log data that includes voice queries spoken by a particular user. The actions further include based on the voice query log data that includes voice queries spoken by a particular user, determining a pause threshold from the voice query log data that includes voice queries spoken by the particular user. The actions further include receiving, from the particular user, an utterance. The actions further include determining that the particular user has stopped speaking for at least a period of time equal to the pause threshold. The actions further include based on determining that the particular user has stopped speaking for at least a period of time equal to the pause threshold, processing the utterance as a voice query.

Systems and processes for operating an intelligent automated assistant are provided. An example process includes initiating a virtual assistant session responsive to receiving user input. In accordance with initiating the virtual assistant session, the process includes determining, based on data obtained using one or more sensors of the electronic device, whether one or more criteria representing expressed user disinterest are satisfied. In accordance with determining that the one or more criteria representing expressed user disinterest are satisfied prior to a first time, the process includes automatically deactivating the virtual assistant session prior to the first time. The first time is defined by a setting of the electronic device. In accordance with determining that the one or more criteria representing expressed user disinterest are not satisfied prior to the first time, the process includes automatically deactivating the virtual assistant session at the first time.

Systems and processes for operating an intelligent automated assistant are provided. An example process includes initiating a virtual assistant session responsive to receiving user input. In accordance with initiating the virtual assistant session, the process includes determining, based on data obtained using one or more sensors of the electronic device, whether one or more criteria representing expressed user disinterest are satisfied. In accordance with determining that the one or more criteria representing expressed user disinterest are satisfied prior to a first time, the process includes automatically deactivating the virtual assistant session prior to the first time. The first time is defined by a setting of the electronic device. In accordance with determining that the one or more criteria representing expressed user disinterest are not satisfied prior to the first time, the process includes automatically deactivating the virtual assistant session at the first time.

A method of own voice detection is provided for a user of a device. A first signal is detected, representing air-conducted speech using a first microphone of the device. A second signal is detected, representing bone-conducted speech using a bone-conduction sensor of the device. The first signal is filtered to obtain a component of the first signal at a speech articulation rate, and the second signal is filtered to obtain a component of the second signal at the speech articulation rate. The component of the first signal at the speech articulation rate and the component of the second signal at the speech articulation rate are compared, and it is determined that the speech has not been generated by the user of the device, if a difference between the component of the first signal at the speech articulation rate and the component of the second signal at the speech articulation rate exceeds a threshold value.

A method of own voice detection is provided for a user of a device. A first signal is detected, representing air-conducted speech using a first microphone of the device. A second signal is detected, representing bone-conducted speech using a bone-conduction sensor of the device. The first signal is filtered to obtain a component of the first signal at a speech articulation rate, and the second signal is filtered to obtain a component of the second signal at the speech articulation rate. The component of the first signal at the speech articulation rate and the component of the second signal at the speech articulation rate are compared, and it is determined that the speech has not been generated by the user of the device, if a difference between the component of the first signal at the speech articulation rate and the component of the second signal at the speech articulation rate exceeds a threshold value.

A natural-language voice chatbot engages a consumer in a voice dialogue. The chatbot is customized for engaging the specific consumer based on features and characteristics of that specific consumer’s speech and a lexicon associated with terms, words, and commands for item ordering. The consumer can perform voice queries for specific items and/or specific establishments for placing a pre-staged order with the chatbot. Once the consumer selects options with a specific establishment, a pre-staged order is provided to the corresponding establishment on behalf of the user. Location data for a consumer-operated device is monitored and when it is determined that the consumer will arrive at the establishment within a time period required by the establishment to prepare the pre-staged order, a message is sent to the establishment to begin preparing the pre-staged order.

A method to adaptively predict non-default actions against unstructured utterances by an automated assistant operating in a computing-system is provided. The method includes extracting voice-features based on receiving an input utterance from at-least one speaker by an automatic speech recognition (ASR) device, identifying the input utterance as an unstructured utterance based on the extracted voice-features and a mapping between the input utterance with one or more default actions as drawn by the ASR, obtaining at least one probable action to be performed in response to the unstructured utterance through a dynamic bayesian network (DBN). The method further includes providing the at least one probable action obtained by the DBN to the speaker in an order of the posterior probability with respect to each action.

Training a user-specific perturbation generator for an audio feature detection model includes receiving one or more positive audio samples of a user, each of the one or more positive audio samples including an audio feature; receiving one or more negative audio samples of the user, each of the one or more negative audio samples sharing an acoustic similarity with at least one of the one or more positive audio samples; and adversarially training a user-specific perturbation generator model to generate a user-specific perturbation, the training based on the one or more positive audio samples and the one or more negative audio samples. Perturbing audio samples of the user with the user-specific perturbation can cause an audio feature detection model to recognize the audio feature in audio samples that include the audio feature and/or to refrain from recognizing the audio feature in audio samples that do not include the audio feature.

In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.