Abstract: A method and a decoder device of generating a concealment audio subframe of an audio signal are provided. The method comprises generating frequency spectra on a subframe basis where consecutive subframes of the audio signal have a property that an applied window shape of first subframe of the consecutive subframes is a mirrored version or a time reversed version of a second subframe of the consecutive subframes. Peaks of a signal spectrum of a previously received audio signal are detected for a concealment subframe, and a phase of each of the peaks is estimated. A time reversed phase adjustment is derived based on the estimated phase and applied to the peaks of the signal spectrum to form time reversed phase adjusted peaks.

Abstract: Systems and methods are provided for optimizing and securing an enterprise voice service accessed by an external voice assistant device. An enterprise voice assistant installed on a client device acts as an enterprise voice service for an external voice assistant device. The enterprise voice assistant receives a voice query from the external voice assistant device. The voice query is processed using a machine learning model to extract an intent and at least one slot. The extracted intent and at least one slot are used to determine whether a response to the voice query can be generated using local enterprise data that was previously received and stored by the client device from a management server. The response is generated based on the determination by using the local enterprise data or by sending the extracted intent and at least one slot to and receiving the response from the management server.

Abstract: A signal processing method to determine whether or not a detected key-phrase is spoken by a wearer of a headphone. The method receives an accelerometer signal from an accelerometer in a headphone and receives a microphone signal from at least one microphone in the headphone. The method detects a key-phrase using the microphone signal and generates a voice activity detection (VAD) signal based on the accelerometer signal. The method determines whether the VAD signal indicates that the detected key-phrase is spoken by a wearer of the headphone. Responsive to determining that the VAD signal indicates that the detected key-phrase is spoken by the wearer of the headphone, triggering a virtual personal assistant (VPA).

Abstract: The present disclosure describes systems and methods for extensible search, content, and dialog management. Embodiments of the present disclosure provide a dialog system with a trained intent recognition model (e.g., a deep learning model) to receive and understand a natural language query from a user. In cases where intent is not identified for a received query, the dialog system generates one or more candidate responses that may be refined (e.g., using human-in-the-loop curation) to generate a response. The intent recognition model may be updated (e.g., retrained) the accordingly. Upon receiving a subsequent query with similar intent, the dialog system may identify the intent using the updated intent recognition model.

Abstract: A user input in a source language is received. A set of contextual data is received. The user input is encoded into a user input feature vector. The set of contextual data is encoded into a context feature vector. The user input feature vector and the context feature vector are used to generate a fusion vector. An adaptive neural network is trained to identify a second context feature vector, based on the fusion vector. A second user input in the source language is received for translation into a target language. The adaptive neural network is used to determine, based on the second context feature vector, a second user input feature vector. The second user input feature vector is decoded, based on the source language and the target language, into a target language output. A user is notified of the target language output.

Abstract: A method, computer system, and a computer program product for speech synthesis is provided. The present invention may include generating one or more final voiceprints. The present invention may include generating one or more voice clones based on the one or more final voiceprints. The present invention may include classifying the one or more voice clones into a grouping using a language model, wherein the language model is trained using manually classified uncloned voice samples. The present invention may include identifying a cluster within the grouping, wherein the cluster is identified by determining a difference between corresponding vectors of the one or more voice clones below a similarity threshold. The present invention may include generating a new archetypal voice by blending the one or more voice clones of the cluster where the difference between the corresponding vectors is below the similarity threshold.

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: There is provided automatic detection of pronunciation errors in spoken words utilizing a neural network model that is trained for a target phoneme. The target phoneme may be a phoneme in English language. The pronunciation errors may be detected in English words.

Abstract: Systems and methods for handling multilingual queries are provided. One example method includes receiving, at a computing device, an input, wherein the input comprises a multi-lingual query comprising at least a first source language and a second source language. The multi-lingual query is translated, word for word, into a destination language to produce a monolingual query, with the word order of the multilingual query and the word order of the monolingual query being the same. The monolingual query is processed using natural language processing to map the mono-lingual query to a natural language query in the destination language.

Abstract: An apparatus for processing a multichannel audio signal has a plurality of channel signals. The apparatus performs a time scale modulation of the multichannel audio signal and has a phase adaptor and a separator. The phase adaptor provides a processed signal by modifying a phase of a signal based on a combination of the channel signals. The separator provides separated signals based on the processed signal. A corresponding method is provided.

Abstract: In one aspect, a method includes detecting a fingerprint match between query fingerprint data representing at least one audio segment within podcast content and reference fingerprint data representing known repetitive content within other podcast content, detecting a feature match between a set of audio features across multiple time-windows of the podcast content, and detecting a text match between at least one query text sentences from a transcript of the podcast content and reference text sentences, the reference text sentences comprising text sentences from the known repetitive content within the other podcast content. The method also includes responsive to the detections, generating sets of labels identifying potential repetitive content within the podcast content. The method also includes selecting, from the sets of labels, a consolidated set of labels identifying segments of repetitive content within the podcast content, and responsive to selecting the consolidated set of labels, performing an action.

Abstract: Systems and methods are disclosed for a voice/chatbot building system. The voice/chatbot builder may involve receiving an identified intent, receiving a task related to the identified intent, and receiving a response related to both the identified intent and the task. The identified intent, task, and response may form a first conversation. The first conversation may be linked to other conversations to establish contextual relationships among conversations and determine conversation priority. Voice/chatbot building may also train natural language processing machine learning algorithms.

Abstract: An inter-channel phase difference (IPD) parameter extraction method includes obtaining a parameter for obtaining an information extraction manner for a current frame of a multi-channel signal; obtaining an IPD parameter extraction manner for the current frame based on the parameter for obtaining the information extraction manner, where the obtained IPD parameter extraction manner is one of at least two preset IPD parameter extraction manners; and obtaining an IPD parameter of the current frame based on the obtained IPD parameter extraction manner for the current frame.

Abstract: A conversation design is received for a conversation bot that enables the conversation bot to provide a service using a conversation flow specified at least in part by the conversation design. The conversation design specifies in a first human language at least a portion of a message content to be provided by the conversation bot. It is identified that an end-user of the conversation bot prefers to converse in a second human language different from the first human language. In response to a determination that the message content is to be provided by the conversation bot to the end-user, the message content of the conversation design is dynamically translated for the end-user from the first human language to the second human language. The translated message content is provided to the end-user in a message from the conversation bot.

Abstract: A computing device translates a spoken word into a corresponding ordered set of spoken phonemes and analyzes correctness of the spoken word relative to a target word. The analyzing includes attempting to locate each of the spoken phonemes in an ordered set of grapheme-phoneme correspondences (GPCs) describing the target word, and determining whether or not the ordered set of spoken phonemes comprises a same number of phonemes as in the ordered set of GPCs. The analyzing also includes comparing the order of the ordered set of spoken phonemes against the order of the ordered set of GPCs. The computing device generates a report, based on the analyzing, that identifies at least one of the GPCs in the ordered set of GPCs as having been incorrectly applied in decoding the target word.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag. The high frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.

Abstract: Techniques for enrolling a user in a system's user recognition functionality without requiring the user speak particular speech are described. The system may determine characteristics unique to a user input. The system may generate an implicit voice profile from user inputs having similar characteristics. After an implicit voice profile is generated, the system may receive a user input having speech characteristics similar to that of the implicit voice profile. The system may ask the user if the user wants the system to associate the implicit voice profile with a particular user identifier. If the user responds affirmatively, the system may request an identifier of a user profile (e.g., a user name). In response to receiving the user's name, the system may identify a user profile associated with the name and associate the implicit voice profile with the user profile, thereby converting the implicit voice profile into an explicit voice profile.

Abstract: A system for automated text anonymisation of clinical text, the system including an AI pipeline module to configure symbolic AI pipeline components for detecting protected health information (PHI) in the clinical text; a masking module for masking the detected PHI in the clinical text and generating a de-identified clinical text output file as well as a corresponding label file with de-identified information. The pipeline components may include at least one non-symbolic AI pipeline component or machine learning model.

Abstract: Dynamic interfacing with applications is provided. For example, a system receives a first input audio signal. The system processes, via a natural language processing technique, the first input audio signal to identify an application. The system activates the application for execution on the client computing device. The application declares a function the application is configured to perform. The system modifies the natural language processing technique responsive to the function declared by the application. The system receives a second input audio signal. The system processes, via the modified natural language processing technique, the second input audio signal to detect one or more parameters. The system determines that the one or more parameters are compatible for input into an input field of the application. The system generates an action data structure for the application. The system inputs the action data structure into the application, which executes the action data structure.

Abstract: An electronic device for speech recognition includes a multi-channel microphone array required for remote speech recognition. The electronic device improves efficiency and performance of speech recognition of the electronic device in a space where noise other than speech to be recognized exists. A control method includes receiving a plurality of audio signals output from a plurality of sources through a plurality of microphones and analyzing the audio signals and obtaining information on directions in which the audio signals are input and information on input times of the audio signals. A target source for speech recognition among the plurality of sources is determined on the basis of the obtained information on the directions in which the plurality of audio signals are input, and the obtained information on the input times of the plurality of audio signals, and an audio signal obtained from the determined target source is processed.