Abstract: A system and method for voice control of operating room electrical equipment. The system comprises an electrosurgical generator a controller with a memory, a graphical user interface controlled by said controller, a power module, a field programmable gate array, and a voice recognition module connected to said field programmable gate array, a data storage connected to said controller in said electrosurgical generator; and electrical operating room equipment connected to said voice recognition module, wherein said electrical operating room equipment is configured to receive and decrypt encrypted commands from said voice recognition module. The electrical operating room equipment may said electrosurgical generator or a robotic surgical system or other electrical equipment in an operating room. The connection between said electrical operating room equipment and said voice recognition module may be wireless.

Abstract: A system includes an interactive voice recognition (IVR) module arranged to perform a speech conversation with a first user and receive a first user identifier, where the speech conversation has an interaction context based on a subject matter of the speech conversation. The system includes a datastore arranged to store a group of active words associated with the interaction context and store first user voiceprints derived from pre-captured audio of the first user, where each active word is selected based on one or more selection criterion derived from conversations of a population of users. An automated speech recognition (ASR) module is arranged to perform speech recognition of the first user audio provided during the speech conversation. A voice biometric (VB) module is arranged to generate captured voiceprints and determine a similarity score based on comparisons of captured voiceprints with first user voiceprints.

Abstract: Method and system to control a conversational bot uses a directed acyclic graph to specify a desired conversation flow. A graph node has synthetic conversation transcripts annotated with events, wherein an event in a synthetic conversation transcript has preconfigured event expressions that represent ways in which dialogue at the node can unfold. During an on-going conversation with an actor, the system provides a data model uniquely associated with the conversation and that specifies a linear sequence of observations. The data model includes events representing semantically-related conversation fragments located in annotated historical conversation transcripts. In response to receipt of an input in association with a current graph node, the system determines whether the input extends an event in the synthetic conversation transcript associated with the node. If so, a response that continues a current conversation flow in the graph is provided.

Abstract: Techniques for naming devices via voice commands are described herein. For instance, a user may issue a voice command to a voice-controlled device stating, “you are the kitchen device”. Thereafter, the device may respond to voice commands directed, by name, to this device. For instance, the user may issue a voice command requesting to “play music on my kitchen device”. Given that the user has configured the device to respond to this name, the device may respond to the command by outputting the requested music.

Abstract: Methods and systems for performing multi-task learning of query intent and named entities are provided. One method includes receiving a query comprising query text. The method further includes providing the query text to a neural network model implemented on a computing system, the neural network model having a plurality of layers, wherein at least one layer comprises a plurality of loss functions including a named entity tag learning loss function and an intent classification loss function. The method also includes obtaining, from the neural network model, an identification of a named entity and a query intent derived from the query text. A query response may be formulated based, at least in part, on the named entity and query intent.

Abstract: A multi-channel signal encoding method includes obtaining a multi-channel signal of a current frame; determining an initial multi-channel parameter of the current frame; determining a difference parameter based on the initial multi-channel parameter of the current frame and multi-channel parameters of previous K frames of the current frame, where the difference parameter represents a difference between the initial multi-channel parameter of the current frame and the multi-channel parameters of the previous K frames, and K is an integer greater than or equal to one; determining a multi-channel parameter of the current frame based on the difference parameter and a characteristic parameter of the current frame; and encoding the multi-channel signal based on the multi-channel parameter of the current frame.

Abstract: An audio encoder for encoding an audio signal includes: a first encoding processor for encoding a first audio signal portion in a frequency domain, wherein the first encoding processor includes: a time frequency converter for converting the first audio signal portion into a frequency domain representation having spectral lines up to a maximum frequency of the first audio signal portion; a spectral encoder for encoding the frequency domain representation; a second encoding processor for encoding a second different audio signal portion in the time domain; a cross-processor for calculating, from the encoded spectral representation of the first audio signal portion, initialization data of the second encoding processor, so that the second encoding processing is initialized to encode the second audio signal portion immediately following the first audio signal portion in time in the audio signal; a controller configured for analyzing the audio signal and for determining, which portion of the audio signal is the firs

Abstract: [Problem] Provided is a system that can objectively evaluate a person who makes a presentation (presenter) [Solution] A presentation evaluation system 1 includes: a voice analysis unit 3 that analyzes a content of a conversation, a presentation material related information storage unit 5 that stores information related to a presentation material, a keyword storage unit 7 that stores information related to a keyword in each page of the presentation material, a related term storage unit 9 that stores a related term of each keyword, and an evaluation unit 11 that evaluates the content of the conversation analyzed by the voice analysis unit 3 or a person who had the conversation.

Abstract: According to some disclosed embodiments an action is performed by an electronic social agent. The electronic social agent collects a first dataset indicating the user's state, the user's environment state, and a first user response to the performed action. Then, it is determined whether it is desirable to collect a second response from the user and, if so, it is further determined whether to generate a question to be presented to the user based on an analysis of a first dataset and the first user response. Then, an optimal time for presenting the question to the user is determined. A question that is based on the collected data and the first user response is generated by the electronic social agent for actively collecting an additional user response. Then, based on the collected additional user response, the decision-making model of the electronic social agent is updated and improved.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. In one example process, a speech input is received from a user. In response to determining that the speech input corresponds to a user intent of obtaining information associated with a user experience of the user, one or more parameters referencing a user experience of the user are identified. Metadata associated with the referenced user experience is obtained from an experiential data structure. Based on the metadata, one or more media items associated with the referenced are retrieved based on the metadata. The one or more media items associated with the referenced user experience are output together.

Abstract: Embodiments of the present invention provide methods and systems for performing automatic diarisation of sound recordings including speech from one more speakers. The automatic diarisation has a development or training phase and a utilisation or evaluation phase. In the development or training phase background models and hyperparameters are generated from already annotated sound recordings. These models and hyperparameters are applied during the evaluation or utilisation phase to diarise new or not previously diarised or annotated recordings.

Abstract: [Problem] To provide a system capable of correcting an error in speech recognition by easier work than in the prior art. [Solution] A conversation analysis system comprising: a speech analysis unit 3 for analyzing contents contained in a conversation; a topics grasping unit 5 for grasping topics of the conversation; a topics-related correction terms storage unit 7 for, with respect to the respective topics, storing pronunciations to be speech-recognized or speech-recognized terms, and candidates for correction terms related to the pronunciations to be speech-recognized or the speech-recognized terms; and a conversation correction unit 9 for correcting the conversation analyzed by the speech analysis unit 3.

Abstract: A method for training a dialog generation model includes: acquiring a dialog data set, and encoding a post in the dialog data set by using an encoder in the dialog generation model to obtain an encoded representation of the post; fusing, by using a decoder in the dialog generation model, the encoded representation of the post and knowledge information corresponding to the post that is obtained from a knowledge base question answering model through transfer learning to obtain a predicted response corresponding to the post; determining a value of a loss function of the dialog generation model based on the predicted response and the annotated response that correspond to the post; and updating a model parameter of the dialog generation model based on the value of the loss function. A method, an apparatus, a device, and a computer storage medium for generating a dialog are also provided.

Abstract: Disclosed is a method of identifying, by at least one processor of a device, a cognitive function state of a user. The method may include inputting user information, at least one voice data, and speech time information related to the at least one voice data to a cognitive function state identification model, and identifying a cognitive function state of the user based on first output data that is output by the cognitive function state identification model.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. In one example process, a speech input is received from a user. In response to determining that the speech input corresponds to a user intent of obtaining information associated with a user experience of the user, one or more parameters referencing a user experience of the user are identified. Metadata associated with the referenced user experience is obtained from an experiential data structure. Based on the metadata, one or more media items associated with the referenced are retrieved based on the metadata. The one or more media items associated with the referenced user experience are output together.

Abstract: A method for maintaining a dialogue state associated with a dialogue between a user and a digital system includes receiving, by a dialogue state tracker associated with the digital system, a representation of a user communication, updating, by the dialogue state tracker, the dialogue state and providing a system response based on the updated dialogue state. The dialogue state is updated by evaluating, based on the representation of the user communication, a plurality of member scores corresponding to a plurality of ontology members of an ontology set, and selecting, based on the plurality of member scores, zero or more of the plurality of ontology members to add to or remove from the dialogue state.

Abstract: Various embodiments of the present disclosure evaluate transcription accuracy. In some implementations, the system normalizes a first transcription of an audio file and a baseline transcription of the audio file. The baseline transcription can be used as an accurate transcription of the audio file. The system can further determine an error rate of the first transcription by aligning each portion of the first transcription with the portion of the baseline transcription, and assigning a label to each portion based on a comparison of the portion of the first transcription with the portion of the baseline transcription.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a dialog system using user feedback. According to one implementation, a method includes receiving, by a dialog engine and from a computing device, a voice input; determining, by the dialog engine, that the received voice input is classified as feedback to an answer of a question; identifying, by the dialog engine, a predetermined feedback score associated with the feedback; and adjusting a confidence score associated with the question and the answer based on the predetermined feedback score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for suppressing hotword triggers when detecting a hotword in recorded media are disclosed. In one aspect, a method includes the actions of receiving, by a computing device, audio corresponding to playback of an item of media content. The actions further include determining, by the computing device, that the audio includes an utterance of a predefined hotword and that the audio includes an audio watermark. The actions further include analyzing, by the computing device, the audio watermark. The actions further include based on analyzing the audio watermark, determining, by the computing device, whether to perform speech recognition on a portion of the audio following the predefined hotword.

Abstract: Methods, systems, and apparatus for performing speech recognition. In some implementations, acoustic data representing an utterance is obtained. The acoustic data corresponds to time steps in a series of time steps. One or more computers process scores indicative of the acoustic data using a recurrent neural network to generate a sequence of outputs. The sequence of outputs indicates a likely output label from among a predetermined set of output labels. The predetermined set of output labels includes output labels that respectively correspond to different linguistic units and to a placeholder label that does not represent a classification of acoustic data. The recurrent neural network is configured to use an output label indicated for a previous time step to determine an output label for the current time step. The generated sequence of outputs is processed to generate a transcription of the utterance, and the transcription of the utterance is provided.