Abstract: In an example, a text sentence comprising a plurality of words is obtained. Each of the plurality of words is passed through a deep compositional character-to-word model to encode character-level information of each of the plurality of words into a character-to-word expression. The character-to-word expressions are combined with pre-trained word embeddings. The combined character-to-word expressions and pre-trained word embeddings are fed into one or more bidirectional long short-term memories to learn contextual information for each of the plurality of words. Then, sequential conditional random fields are applied to the contextual information for each of the plurality of words.

Abstract: Techniques regarding speech characterization are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a speech analysis component that can determine a condition of an origin of an audio signal based on a difference between a first feature of the audio signal and a second feature of a synthesized reference audio signal.

Abstract: In one example, a system can execute a first machine-learning model to determine an overall classification for a textual dataset. The system can also determine classification scores indicating the level of influence that each token in the textual dataset had on the overall classification. The system can select a first subset of the tokens based on their classification scores. The system can also execute a second machine-learning model to determine probabilities that the textual dataset falls into various categories. The system can determine category scores indicating the level of influence that each token had on a most-likely category determination. The system can select a second subset of the tokens based on their category scores. The system can then generate a first visualization depicting the first subset of tokens color-coded to indicate their classification scores and a second visualization depicting the second subset of tokens color-coded to indicate their category scores.

Abstract: Concealing a lost audio frame of a received audio signal is provided by performing a sinusoidal analysis (81) of a part of a previously received or reconstructed audio signal, wherein the sinusoidal analysis involves identifying frequencies of sinusoidal components of the audio signal, applying a sinusoidal model on a segment of the previously received or reconstructed audio signal, wherein said segment is used as a prototype frame in order to create a substitution frame for a lost audio frame, and creating the substitution frame (83) for the lost audio frame by time-evolving sinusoidal components of the prototype frame, up to the time instance of the lost audio frame, in response to the corresponding identified frequencies.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech separation. One of the methods includes obtaining a recording comprising speech from a plurality of speakers; processing the recording using a speaker neural network having speaker parameter values and configured to process the recording in accordance with the speaker parameter values to generate a plurality of per-recording speaker representations, each speaker representation representing features of a respective identified speaker in the recording; and processing the per-recording speaker representations and the recording using a separation neural network having separation parameter values and configured to process the recording and the speaker representations in accordance with the separation parameter values to generate, for each speaker representation, a respective predicted isolated audio signal that corresponds to speech of one of the speakers in the recording.

Abstract: Disclosed are a method and an apparatus for editing audio, an electronic device and a storage medium. The method includes: acquiring a modified text obtained by modifying a known original text of an audio to be edited according to a known text for modification; predicting a duration of an audio corresponding to the text for modification; adjusting a region to be edited of the audio to be edited according to the duration of the audio corresponding to the text for modification, to obtain an adjusted audio to be edited; obtaining, based on a pre-trained audio editing model, an edited audio according to the adjusted audio to be edited and the modified text. In the present disclosure, the edited audio obtained by the audio editing model sounds natural in the context, and supports the function of synthesizing new words that do not appear in the corpus.

Abstract: The disclosure describes one or more embodiments of an acoustic improvement system that accurately and efficiently determines and provides actionable acoustic improvement suggestions to users for digital audio recordings via an interactive graphical user interface. For example, the acoustic improvement system can assist users in creating high-quality digital audio recordings by providing a combination of acoustic quality metrics and actionable acoustic improvement suggestions within the interactive graphical user interface customized to each digital audio recording. In this manner, all users can easily and intuitively utilize the acoustic improvement system to improve the quality of digital audio recordings.

Abstract: An electronic device and method of recognizing an audio scene are provided.

Abstract: The technology of the present application provides software as a service (SaaS) executing on a server in a cloud or network. The SaaS receives data from a mobile device of a user over the network. The SaaS processes the data and returns the processed data to a client application executing on a client device of the user, which user is the same as the user of the mobile device wherein there is no direct communication link, wireless or wired, between the mobile device and the client device. In one aspect, the technology of the present application provides the mobile device as a smartphone and a microphone application to be executed on the smartphone.

Abstract: A method, computer program product, and computing system for obtaining calibration information for a three-dimensional space incorporating an ACI system; and processing the calibration information to calibrate the ACI system.

Abstract: A speech-processing system capable of receiving and processing audio data to determine if the audio data includes speech that was intended for the system. Non-system directed speech may be filtered out while system-directed speech may be selected for further processing. A system-directed speech detector may use a trained machine learning model (such as a deep neural network or the like) to process a feature vector representing a variety of characteristics of the incoming audio data, including the results of automatic speech recognition and/or other data. Using the feature vector the model may output an indicator as to whether the speech is system-directed. The system may also incorporate other filters such as voice activity detection prior to speech recognition, or the like.

Abstract: A method and computing platform to imitate human conversational response as a context transitions across multiple channels (e.g., chat, messaging, email, voice, third party communication, etc.) where inputs to the system are categorized into identified speech acts and physical acts, and a conversational bot is associated to the channels. In this approach, a data model associated with a multi-turn conversation is provided. The data model comprises an observation history, wherein an observation in the observation history includes an identification of a channel in which the observation originates. As turns are added to the multi-turn conversation, a conversational context across multiple channels is persisted using the data model. Using this approach, an AI-supported conversation started in one channel can move to another conversation channel while maintaining the context of the conversation intact and coherent.

Abstract: A method and device for training an acoustic model are provided. The method comprises determining a plurality of tasks for training an acoustic model, obtaining resource occupancies of nodes participating in the training of the acoustic model, and distributing the tasks to the nodes according to the resource occupancies of the nodes and complexities of the tasks. By using computational resources distributed at multiple nodes, tasks for training an acoustic model are performed in parallel in a distributed manner, so as to improve training efficiency.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: A method and method for natural language generation employ a natural language generation model which has been trained to assign an utterance label to a new text sequence, based on features extracted from the text sequence, such as parts-of-speech. The model assigns an utterance label to the new text sequence, based on the extracted features. The utterance label is used to guide the generation of a natural language utterance, such as a question, from the new text sequence. The system and method find application in dialog systems for generating utterances, to be sent to a user, from brief descriptions of problems or solutions in a knowledge base.

Abstract: Methods, apparatus, and computer readable media are described related to recording, organizing, and making audio files available for consumption by voice-activated products. In various implementations, in response to receiving an input from a first user indicating that the first user intends to record audio content, audio content may be captured and stored. Input may be received from the first user indicating at least one identifier for the audio content. The stored audio content may be associated with the at least one identifier. A voice input may be received from a subsequent user. In response to determining that the voice input has particular characteristics, speech recognition may be biased in respect of the voice input towards recognition of the at least one identifier. In response to recognizing, based on the biased speech recognition, presence of the at least one identifier in the voice input, the stored audio content may be played.

Abstract: An information processing device and information processing method capable of outputting an action based on an intention of the user. The information processing device including an action deciding unit that determines an action for a user on a basis of a distance from the user and an output control unit that outputs the action.

Abstract: A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: A computer-implemented technique is described for enabling a user to create a conversational bookmark in the course of the user's interaction with a BOT. The bookmark designates a particular juncture in the user's interaction with the BOT. When the user later invokes the bookmark, the computer-implemented technique resumes the user's interaction with the BOT, starting at the particular juncture. The technique can accomplish the above functions in a BOT-independent manner (which does not involve changes to the BOT) or a BOT-dependent manner (which involves changes to the BOT). The technique can also be extended to a task of creating and activating bookmarks in the course of a conversation among two or more humans.