Abstract: The control device includes a control unit. The control unit recognizes a voice command transmitted from a telephone terminal outside the vehicle. The control unit interprets the operation content requested by the recognized voice command. The control unit selects one or more positions at which the sound is picked up by the microphone in the vehicle cabin based on the interpreted operation content. The control unit picks up a voice uttered at one or more selected positions by a microphone and transmits the picked up voice to a telephone terminal.

Abstract: A device may be configured to parse a syntax element specifying the number of available languages within a presentation associated with an audio stream. A device may be configured to parse one or more syntax elements identifying each of the available languages and parse an accessibility syntax element for each language within the presentation.

Abstract: A method and an apparatus are disclosed for a speaker verification. The apparatus may comprise at least one processor configured to execute instructions to perform generating, based on an utterance of a speaker to a deep learning-based speaker verification model, a speaker embedding with a preset dimension, verifying, based on the speaker embedding, the speaker, and detecting, based on the verified speaker, an identity of a user associated with the apparatus.

Abstract: Methods and systems for processing of voice input to identify intents and mapped standard terminologies are provided. Using natural language processing, an intent of a voice input is identified. The intent is utilized to identify a standard terminology that maps to the intent. The standard terminology is utilized to identify information relevant to the standard terminology in a patient's electronic health record.

Abstract: In general, the subject matter described in this specification can be embodied in methods, systems, and program products for receiving a voice query at a mobile computing device and generating data that represents content of the voice query. The data is provided to a server system. A textual query that has been determined by a speech recognizer at the server system to be a textual form of at least part of the data is received at the mobile computing device. The textual query is determined to include a carrier phrase of one or more words that is reserved by a first third-party application program installed on the computing device. The first third-party application is selected, from a group of one or more third-party applications, to receive all or a part of the textual query. All or a part of the textual query is provided to the selected first application program.

Abstract: Techniques for fine-tuning language models using target language vocabulary and parallel data for machine translation are disclosed. In an example method, a computing device receives one or more words in a source language. The computing device translates the one or more words to a target language using a fine-tuned language model. The language model is fine-tuned by accessing a foundation language model pre-trained using source language training data and having a source language vocabulary. A set of tokens is added to the model representing a target language vocabulary. The model is further trained using target language training data. The model is further trained using parallel training data including a number of parallel elements each parallel element including words in the source language and the corresponding words in the target language. The computing device outputs the translated one or more words in the target language.

Abstract: Methods and systems for using voice (e.g., voice commands) to control a plurality of network devices via a motion sensing control device are provided. A control device can detect movement (e.g., a gesture) associated with the control device. Based on the movement satisfying a predefined condition, the control device can initiate a either a direct or a proxy communication session with a remote computing device. The communication session can be established and maintained for a predefined period such that data associated with a command can be immediately transmitted to the remote computing device. Thus, when a voice command is received, data associated with the command can be transmitted over the already established communication session to the remote computing device. The remote computing device can provide a response to the control device and/or transmit a command code associated with the voice command to one or more devices intended to be controlled.

Abstract: A speech-processing system may provide access to multiple virtual assistants via a user device. A user may invoke a particular virtual assistant by speaking its wakeword. The virtual assistants may send notifications to the user via the user device, which may present an indication of the available notifications. The user may request delivery of the notifications using, for example, a voice user interface. When delivering notifications from multiple virtual assistants, the system may use distinct voice and/or visual characteristics to indicate a source of the notifications. For example, after delivering a first notification corresponding to a first virtual assistant (e.g., the one invoked by the user at the beginning of the interaction) the first virtual assistant may offer to deliver a second notification from a second virtual assistant. The second virtual assistant may deliver the second notification using voice/visual characteristics different from those used for the first notification.

Abstract: Systems and methods for audio processing include capturing first sound data via at least one microphone of a network microphone device (NMD) and determining, via a voice activity detection process, that the first sound data does not include voice activity. The first sound data is stored in a buffer, and the NMD forgoes spatial processing of the first sound data. The NMD can capture second sound data and determine, via the voice activity process, that the second sound data includes voice activity. The NMD spatially processes the second sound data to produce filtered sound data. The NMD detects a wake word based on data in the buffer. After detecting the wake word, the NMD may determine an action to be performed based on the data in the buffer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sanitizing artificial intelligence prompts. One of the methods includes receiving a message a) for an external system and b) that comprises two or more phrases; for at least one phrase from the two or more phrases: determining a context of the phrase in the message; determining, using the context, whether modification of the phrase will likely maintain an intent of the message; determining whether to permit unedited transmission of the message to the external system using a result of at least one of one or more determinations whether modification of the phrase will likely maintain the intent of the message; and performing one or more actions using a result of the determination whether to permit unedited transmission of the message to the external system.

Abstract: A method and apparatus for encoding/decoding audio signal are provided. The encoding method includes transforming an input audio signal in a time domain into an audio signal in a frequency domain, quantizing energy of a frequency band of the audio signal in the frequency domain, generating a normal signal by normalizing the audio signal in the frequency domain according to quantized energy, obtaining a feature vector including information on the energy of the frequency band based on the normal signal and the input audio signal, quantizing the feature vector, obtaining a scale factor used to scale the normal signal based on the quantized feature vector, quantizing an adjustment signal into which the normal signal has been scaled based on the scale factor, and outputting bitstreams based on the quantized energy, the quantized feature vector, and the quantized adjustment signal.

Abstract: A method performed by an encoder. The method comprises determining envelope representation residual coefficients as first compressed envelope representation coefficients subtracted from the input envelope representation coefficients. The method comprises transforming the envelope representation residual coefficients into a warped domain so as to obtain transformed envelope representation residual coefficients. The method comprises applying, at least one of a plurality of gain-shape coding schemes on the transformed envelope representation residual coefficients in order to achieve gain-shape coded envelope representation residual coefficients, where the plurality of gain-shape coding schemes have mutually different trade-offs in one or more of gain resolution and shape resolution for one or more of the transformed envelope representation residual coefficients.

Abstract: The input of a user is monitored and a location of the user and a language of the user are detected. The input is converted to a text string in the detected user language and the converted text string is parsed into parsed tokens. A command line and correlated parameters indicated by the input are recognized based on the parsed tokens. The recognized command line with the assigned parameters is executed.

Abstract: An electronic device and a method of controlling the electronic device are provided. The electronic device includes a microphone, a display, memory, and a processor configured to obtain, based on a user voice being received through the microphone while a first user interface (UI) screen is being displayed, text information corresponding to the user voice by inputting the user voice, obtain first information including information on a command and information on an execution target of the command included in the text information, obtain second information including information on functions corresponding to the plurality of objects and information on texts included in the plurality of objects, identify whether a target object corresponding to the user voice is present from among the plurality of objects, and control the display to display a second UI screen corresponding to the target object for performing an operation corresponding to the command.

Abstract: In some implementations, a method includes displaying, on a display, an environment that includes a representation of a virtual agent that is associated with a sensory characteristic. In some implementations, the method includes selecting, based on the sensory characteristic associated with the virtual agent, a subset of a plurality of sensors to provide sensor data for the virtual agent. In some implementations, the method includes providing the sensor data captured by the subset of the plurality of sensors to the virtual agent in order to reduce power consumption of the device. In some implementations, the method includes displaying a manipulation of the representation of the virtual agent based on an interpretation of the sensor data by the virtual agent.

Abstract: A system for an automated real-time transcription and editing of audio data using interim text, including a processor of an audio transcription server (ATS) node configured to host a machine learning (ML) module coupled to at least one audio source entity and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire audio data from the at least one audio source entity; parse out the audio data to derive features for beam forming and features for speaker diarization; generate a set of classifiers based on the features for beam forming and the features for speaker diarization; provide the set of classifiers to the ML module configured to generate a predictive model for producing at least one speaker identification parameter; identify the speaker based on the at least one speaker identification parameter; continuously transcribe the audio data to generate an interim text associated

Abstract: Systems and processes for a multi-modal digital assistant are provided.

Abstract: A method for identifying and executing a voice command in a continuous listening Internet of Things (IoT) environment, may include: receiving, by at least one IoT device, a voice input in the continuous listening IoT environment; detecting, by the at least one IoT device, an occurrence of at least one non-speech event in a vicinity of at least one other IoT device in the continuous listening IoT environment; determining, by the at least one IoT device, an ambient context associated with the at least one non-speech event; determining, by the at least one IoT device, a correlation between the ambient context and the at least one other IoT device based on an event location of the occurrence of the at least one non-speech event; and determining, by the at least one IoT device, presence of at least one voice command within the voice input based on the correlation.

Abstract: A speech synthesis system is described and may include at least one microphone; a speaker; a sensing system, and memory storing processor-executable instructions, which when executed by the processor, cause the processor to: detect speech-related signals emanating from the subject; generate a variable excitation signal; shape the generated variable excitation signal according to previously stored speech recordings; and cause, from the speaker and based on the shaped variable excitation signal, produced speech content that approximates the matched one or more voice characteristics in the previously stored speech recordings.

Abstract: The present disclosure relates to scalable systems and methods for detecting, labeling, and protecting sensitive data in natural language processing (NLP) environments. This includes NLP applications in artificial intelligence (AI) systems, such as language models (LMs) and generative AI (GenAI). More particularly, the present disclosure introduces a hierarchical, context-aware labeling mechanism that is optimized using an LM in conjunction with machine learning (ML) techniques to ensure the utility-preserving effective protection of sensitive data with, for example, minimal false positives and false negatives and/or optimal precision and recall (e.g., in terms of an F1 Score).