Abstract: This invention relates generally to the field of electronic communications and the transmittance of such communications. More specifically, the invention discloses a new and useful method for delivering a therapeutic through an eco-system of digital content based on a user-mapped EMS. The method comprises: receiving a text input comprising message content from an electronic computing device associated with a user; parsing the message content comprised in the text input for emotionally-charged language; assigning a sentiment value, based on the emotionally-charged language, from a dynamic sentiment value spectrum to the text input; and, based on the sentiment value, imposing a sentiment vector, corresponding to the assigned sentiment value, to the text input, the imposed sentiment vector rendering a sensory effect on the message content designed to convey a corresponding sentiment.

Abstract: During text-to-speech processing, a speech model creates output audio data, including speech, that corresponds to input text data that includes a representation of the speech. A spectrogram estimator estimates a frequency spectrogram of the speech; the corresponding frequency-spectrogram data is used to condition the speech model. A plurality of acoustic features corresponding to different segments of the input text data, such as phonemes, syllable-level features, and/or word-level features, may be separately encoded into context vectors; the spectrogram estimator uses these separate context vectors to create the frequency spectrogram.

Abstract: The present disclosure generally relates to interview training and providing interview feedback. An exemplary method comprises: at an electronic device that is in communication with a display and one or more input devices: receiving, via the one or more input devices, media data corresponding to a user's responses to a plurality of prompts; analyzing the media data; and while displaying, on the display, a media representation of the media data, displaying a plurality of analysis representations overlaid on the media representation, wherein each of the plurality of analysis representations is associated with an analysis of content located at a given time in the media representation and is displayed in coordination with the given time in the media representation.

Abstract: In one embodiment, a method includes receiving, from a client system associated with a first user, a first audio input. The method includes generating multiple transcriptions corresponding to the first audio input based on multiple automatic speech recognition (ASR) engines. Each ASR engine is associated with a respective domain out of multiple domains. The method includes determining, for each transcription, a combination of one or more intents and one or more slots to be associated with the transcription. The method includes selecting, by a meta-speech engine, one or more combinations of intents and slots from the multiple combinations to be associated with the first user input. The method includes generating a response to the first audio input based on the selected combinations and sending, to the client system, instructions for presenting the response to the first audio input.

Abstract: Provided is an artificial intelligence (AI) device for recognizing speech of user. The AI apparatus includes: a microphone; and a processor configured to: receive, via the microphone, a sound signal corresponding to speech of the user, recognize the speech from the sound signal using a language model, determine an intention of the user based on the recognition result, determine whether the determination of the intention is successful, obtain a user's application usage log if the determination of the intention is not successful, and update the language model using the obtained user's application usage log.

Abstract: Methods and systems for natural language processing/understanding of voice conversations are provided. Using natural language processing, a clinical condition is extracted from a voice conversation. A clinical ontology identifies clinical concepts associated with the clinical conditions. The clinical concepts are classified for documentation. The clinical concepts are searched and validated from within an individual's longitudinal record.

Abstract: Techniques for processing complex natural language inputs are described. A complex natural language input may be semantically tagged and parsed to identify individual clauses in the complex natural language input. An execution graph may be generated to represent the clauses and their dependencies. Nodes of the execution graph may be processed using NLU processing and/or a knowledge graph or other information storage and retrieval techniques, and results of such processing may be used to update clause variables with specific entities in the execution graph.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory and a processor configured to control the electronic apparatus to: classify a plurality of input data into a plurality of types to store in the memory, determine at least one among the input data of the classified plurality of types based on a voice command being recognized among the input data, and provide response information corresponding to the voice command based on the input data of the determined type.

Abstract: A method of modelling speech of a user of a headset comprising a microphone, the method comprising: receiving a first sample, from a bone-conduction sensor, representing bone-conducted speech of the user; obtaining a measure of fundamental frequency of the bone-conducted speech in each of a plurality of speech frames of the first sample; obtaining a first distribution of the fundamental frequencies of the bone-conducted speech over the plurality of speech frames; receiving, from the microphone, a second sample; determining a first acoustic condition at the headset based on the second signal; performing a biometric process based on the first distribution of fundamental frequencies and the first acoustic condition.

Abstract: A system is provided for reducing friction during user interactions with a natural language processing system, such as voice assistant systems. The system determines a pre-trained model using dialog session data corresponding to multiple user profiles. The system determines a fine-tuned model using the pre-trained model and a fine-tuning dataset that corresponds to a particular task, such as query rewriting. The system uses the fine-tuned model to process a user input and determine an alternative representation of the input that can result in a desired response from the natural language processing system.

Abstract: Systems and methods for correcting a voice query based on a subsequent voice query with a lower pronunciation rate. In some aspects, the systems and methods calculate first and second pronunciation rates of first and second voice queries. The systems and methods determine that the second pronunciation rate is lower than the first pronunciation rate and determine a first candidate pronunciation time for a first candidate word from the first voice query. The systems and methods determine a second candidate pronunciation time, adjusted to the first pronunciation rate, for the second candidate word from the second voice query. The systems and methods determine that the first candidate pronunciation time matches the second candidate pronunciation time and generate a third voice query based on the first voice query by replacing the first candidate word with the second candidate word.

Abstract: A method of a video call between a first user and a second user is disclosed. The method includes initiating the video call between a first terminal associated with the first user and a second terminal associated with the second user, and determining languages of the first user and the second user who participate in the video call. The method further includes, in case the first user's language is a first language and the second user's language is a second language different from the first language, displaying a transliteration of a greeting of the second language in the first language.

Abstract: A method for increasing stability of an inter-channel time difference (ICTD) parameter in parametric audio coding, wherein a multi-channel audio input signal comprising at least two channels is received. The method comprises obtaining an ICTD estimate, ICTDest(m), for an audio frame m and a stability estimate of said ICTD estimate, and determining whether the obtained ICTD estimate, ICTDest(m), is valid. If the ICTDest(m) is not found valid, and a determined sufficient number of valid ICTD estimates have been found in preceding frames, a hang-over time is determined using the stability estimate and a previously obtained valid ICTD parameter, ICTD(m?1), is selected as an output parameter, ICTD(m), during the hang-over time. The output parameter, ICTD(m), is set to zero if valid ICTDest(m) is not found during the hang-over time.

Abstract: A frequency domain long-term prediction system and method for estimating and applying an optimum long term predictor. Embodiments of the system and method include determining parameters of a single-tap predictor using a frequency-domain analysis having an optimality criteria based on spectral flatness measure. Embodiments of the system and method also include determining parameters of the long-term predictor by accounting for the performance of the vector quantizer in quantizing the various subbands. In some embodiments other encoder metrics (such as signal tonality) are used as well. Other embodiments of the system and method include determining the optimal parameters of the long-term predictor by accounting for some of the decoder operation. Other embodiments of the system and method include extending a 1-tap predictor to a k-th order predictor by convolving the 1-tap predictor with a pre-set filter and selecting from a table of such pre-set filters based on a minimum energy criteria.

Abstract: In one embodiment, a method includes receiving, from a client system associated with a first user, a first audio input. The method includes generating multiple transcriptions corresponding to the first audio input based on multiple automatic speech recognition (ASR) engines. Each ASR engine is associated with a respective domain out of multiple domains. The method includes determining, for each transcription, a combination of one or more intents and one or more slots to be associated with the transcription. The method includes selecting, by a meta-speech engine, one or more combinations of intents and slots from the multiple combinations to be associated with the first user input. The method includes generating a response to the first audio input based on the selected combinations and sending, to the client system, instructions for presenting the response to the first audio input.

Abstract: An embodiment of the present invention provides an artificial intelligence (AI) apparatus for recognizing a speech of a user, the artificial intelligence apparatus includes a memory to store a speech recognition model and a processor to obtain a speech signal for a user speech, to convert the speech signal into a text using the speech recognition model, to measure a confidence level for the conversion, to perform a control operation corresponding to the converted text if the measured confidence level is greater than or equal to a reference value, and to provide feedback for the conversion if the measured confidence level is less than the reference value.

Abstract: The present disclosure includes analyzing a voice command or request from a user, received at an Artificial Intelligence (AI) system, for identifying a possibly incorrect or misunderstood voice command or request. A first user is identified and associated to a first user profile, in response to receiving an audio input, including a question or command, at an AI system. A possible defect or error is identified, in whole or in part, of the question or the command, based on the first profile of the first user and a knowledge corpus. A response by the AI system is determined based on the analysis of the content and the user profile for the first user. A possible alternative content is generated, in whole or in part, for the question or the command, and communicated the response including the possible alternative content to the first user.

Abstract: The system and method delivers a digital therapeutic, specific to an emotional or mental state (EMS) parsed from an electronic message, comprising: an electronic computing device communicatively coupled to a processor. The processor further comprising; an EMS store; a message prescriber; a sentiment vector generator comprising: a parsing module; a coordinate-based sentiment value spectrum comprising one positive to negative-scaled axis and one perpendicular active to passive scaled axis forming a two-dimensional plot of a sentiment value along a positive to negative line (positivity correlate) and an active to passive line (activity correlate).

Abstract: An automated training method enhances conversational effectiveness of a system participant interacting with a virtual client. The method operates on received speech input to produce text. A speech effectiveness analysis processor receives both the speech and text and produces quantified metrics measuring speech effectiveness based on the input parameters. Personalized feedback is generated based on the quantified metrics and is transmitted to the system participant. A virtual client dialog is generated using a dynamic simulation processor. The virtual client dialog is based on the quantified metrics, such that the virtual client responds to the speech input using the generated dialog.

Abstract: A neural network model of a user device is trained to map different words represented in audio data to different points in an N-dimensional embedding space. When the user device determines that a mapped point corresponds to a wakeword, it causes further audio processing, such as automatic speech recognition or natural-language understanding, to be performed on the audio data. The user device may first create the wakeword by first processing audio data representing the wakeword to determine the mapped point in the embedding space.