Abstract: An audio decoder for providing a decoded audio signal representation on the basis of an encoded audio signal representation is configured to adjust decoding parameters in dependence on a configuration information, to decode one or more audio frames using a current configuration information, to compare a configuration information in a configuration structure associated with one or more frames to be decoded by the current configuration information, and to make a transition to perform decoding using the configuration information in the configuration structure associated with the one or more frames to be decoded as a new configuration information if the configuration information in the configuration structure associated with the one or more frames to be decoded, or a relevant portion thereof, is different from the current configuration information, and to consider a stream identifier information included in the configuration structure when comparing the configuration information.

Abstract: The present disclosure relates to determining communication inclusivity amongst speakers during a user communication. Communication inclusivity is a targeted analysis that collectively evaluates speaking opportunities (provided and taken by users) during a user communication and thought completion during speech associated with the user communication. To derive communication inclusivity, a user communication is modeled as a probabilistic interaction between speakers, where a sequence of speaking states of the user communication is identified and analyzed. Non-limiting examples of speaking states comprise: active user speech; periods of silence; overlapping speakers; icon indication; questions in corresponding chat windows; combination states; other contextual signals; and any combination thereof. With these observed sequences of speaking states, a probability distribution is modeled over transitions between states to predict inclusivity of a user communication.

Abstract: Provided is a natural language processing system for determining a merchant based on a natural language query. The system may include a processor to receive a natural language query, convert at least one word of the natural language query to a vector using at least one neural network to form a set of vectors, determine a vector distance from the set of vectors to each profile in a set of profiles, rank the set of profiles based on the vector distance of each profile to the set of vectors, communicate merchant data associated with at least one merchant included in the set of profiles to the user device, receive a selection of a first merchant associated with the merchant data from the user device, and schedule an appointment with the first merchant for a user of the user device. A computer program product and method are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving input voice data synchronous with a visual state of a user interface of the third-party application, generating multiple sentence alternatives for the received input voice data, identifying a best sentence of the multiple sentence alternatives, executing a dialog script for the third-party application using the best sentence, the dialog script generating a response to the received voice data comprising output voice data and a corresponding visual response, and providing the visual response and the output voice data to the third-party application, the third-party application playing the output voice data synchronous with updating the user interface based on the visual response. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Providing, using a computer, personalized captioning in response to a user having difficulty in understanding another participant speaking. Detecting, at a computer, the user having difficulty in understanding another participant speaking, or alternatively, receiving, at the computer, a communication from a user indicating that the user requests assistance to understand speech of a particular participant in an electronic group meeting. The speech of the particular participant is identified, and a speech input is captured from the particular participant in the electronic group meeting. The captured speech of the particular participant is transcribed, and an audio assistance output is generated for communication to the user. The audio assistance output is communicated to a device of the user.

Abstract: Embodiments described herein provide a pipelined natural language question answering system that improves a BERT-based system. Specifically, the natural language question answering system uses a pipeline of neural networks each trained to perform a particular task. The context selection network identifies premium context from context for the question. The question type network identifies the natural language question as a yes, no, or span question and a yes or no answer to the natural language question when the question is a yes or no question. The span extraction model determines an answer span to the natural language question when the question is a span question.

Abstract: An authentication system prevents leakage of a key-reading speech during user authentication based on the key-reading speech of a user reading an authentication key. For each user ID, a storage stores a voiceprint of a user in association with a recorded sound including speech spoken previously by the user. A specifier specifies the user ID of a user attempting to receive authorization. An outputter outputs a masking sound that includes the recorded sound recorded in association with the specified user ID. An acquirer acquires a key-reading speech of the user reading the authentication key and the output masking sound. A remover acquires a second sound by removing the masking sound from the acquired first sound. A determiner determines whether the user has authority pertaining to the specified user ID based on the acquired second sound.

Abstract: A computer-implemented method is provided to optimize natural language processing of voice interaction data in product/service categorization and product/service application. The computer-implemented method receives, from a voice interaction device through a context discovery interface, user voice data corresponding to a user. Furthermore, the computer-implemented method performs, with an NLP engine, natural language processing of the user voice data to determine a context category. Additionally, the computer-implemented method selects, with an AI engine, one of a plurality of context-specific applier interfaces based on the context category. The computer-implemented method automatically transitions, with the AI engine, to said one of the plurality of context-specific applier interfaces. Finally, the computer-implemented method interacts, via the AI engine, with the user via a voice interaction to initiate the product/service application.

Abstract: Embodiments of the present disclosure provide a method and apparatus for processing a multi-language text. According to embodiments of the present disclosure, the multi-language text including contents in a plurality of languages may be encoded with a Unicode. The method further comprises splitting the multi-language text into a plurality of parts based on the Unicode of the multi-language text, contents of the plurality of parts having different languages. In addition, the multi-language text may also be processed based on the plurality of parts.

Abstract: A voice recognition apparatus includes: a communication circuit acquiring a speech sentence that is a result of voice recognition of a speech; a storage storing digit number information indicative of the maximum number of digits; and a control circuit. When the number of digits of a first numerical value indicated by a first numeral included in the speech sentence is larger than the maximum number of digits, the control circuit replaces the first numeral in the speech sentence with a second numeral indicative of a second numerical value having the number of digits equal to or less than the maximum number of digits. The control circuit divides the first numeral into a plurality of numerals and adds numerical values respectively indicated by the plurality of numerals to calculate the second numerical value.

Abstract: A dialog system receives a user request corresponding to a dialog with a user. The dialog system processes the user request to determine multiple service providers capable of responding to the user request. The dialog system selects one service provider based on a request-to-handle score, and selects another service provider based on a satisfaction rating. The dialog system updates the dialog state based on further input provided by the user to determine an output responsive to the user request.

Abstract: A method to transcribe communications may include selecting a first transcription generation technique from among multiple transcription generation techniques for generating transcriptions of audio of one or more communication sessions that involve a user device and obtaining performances of the multiple transcription generation techniques with respect to generating the transcriptions of the audio. The method may also include monitoring comparisons between the performances of the multiple transcription generation techniques and obtaining input from the user with respect to the comparisons. The method may further include selecting a second transcription generation technique from among the multiple transcription generation techniques based on the input from the user.

Abstract: Techniques for intelligently selecting a component to execute with respect to a natural language user input are described. A natural language processing (NLP) system may receive first data representing a natural language input. The NLP system may determine first and second scores representing first and second confidences that first and second components are to be invoked to perform actions responsive to the natural language input, respectively. Based on the first and second scores, the NLP system may determine further information is needed to determine which of the first or second component is to be invoked. The NLP system may query a user for the further information. Based on the further information, the NLP system may determine third and fourth scores representing third and fourth confidences that the first and second components are to be invoked to perform actions responsive to the natural language input, respectively.

Abstract: A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Abstract: A machine has a processor and a memory connected to the processor. The memory stores instructions executed by the processor to supply a name page in response to a request from an administrator machine. Name page updates are received from the administrator machine. The name page updates include participants and associated network contact information for the participants. A code is utilized to form a link to the name page. Prompts for textual name information and audio name information are supplied to a client machine that activates the link to the name page. Textual name information and audio name information are received from the client machine. The textual name information and audio name information are stored in association with the name page. Navigation tools are supplied to facilitate access to the textual name information and audio name information.

Abstract: The present technology relates to a decoding apparatus, a decoding method and a program which make it possible to obtain sound with higher quality. A demultiplexing circuit demultiplexes an input code string into a gain code string and a signal code string. A signal decoding circuit decodes the signal code string to output a time series signal. A gain decoding circuit decodes the gain code string. That is, the gain decoding circuit reads out gain values and gain inclination values at predetermined gain sample positions of the time series signal and interpolation mode information. An interpolation processing unit obtains a gain value at each sample position between two gain sample positions through linear interpolation or non-linear interpolation according to the interpolation mode based on the gain values and the gain inclination values. A gain applying circuit adjusts a gain of the time series signal based on the gain values. The present technology can be applied to a decoding apparatus.

Abstract: A method for providing automatic interpretation may include receiving, by a processor, audible speech from a speech source, generating, by the processor, in real-time, a speech transcript by applying an automatic speech recognition model on the speech, segmenting, by the processor, the speech transcript into speech segments based on a content of the speech by applying a segmenter model on the speech transcript, compressing, by the processor, the speech segments based on the content of the speech by applying a compressor model on the speech segments, generating, by the processor, a translation of the speech by applying a machine translation model on the compressed speech segments, and generating, by the processor, audible translated speech based on the translation of the speech by applying a text to speech model on the translation of the speech.

Abstract: Systems and methods are described for generating a transcript of a legal proceeding or other multi-speaker conversation or performance in real time or near-real time using multi-channel audio capture. Different speakers or participants in a conversation may each be assigned a separate microphone that is placed in proximity to the given speaker, where each audio channel includes audio captured by a different microphone. Filters may be applied to isolate each channel to include speech utterances of a different speaker, and these filtered channels of audio data may then be processed in parallel to generate speech-to-text results that are interleaved to form a generated transcript.

Abstract: Text blocks are semantically compared, and a semantic score is provided to a user. The semantic score is based on application of a machine learning model trained on a text corpus. One or both of the two text blocks may have one or more words that do not appear in the training text corpus (skip-words). Skip-words are used, rather than discarded, to adjust the semantic score via, for example, a penalization function. The user provides feedback about the accuracy of the adjusted semantic score, and the feedback is used to perform supervised learning model.

Abstract: Systems and methods to identify a query parameter in an incoming flight voice or data communication to respond to a request. A processing system configured to: in response to receipt of a clearance message, decode the clearance message to determine whether the clearance message contains a command instruction or clearance data for a flight, and to present the command instruction to a pilot as notice to execute the command instruction or if available, obtain at least one query parameter from the clearance data to configure in a query operation to present in response to a pilot question about the command instruction. In response to receipt of the voice or data communication, determine further an intent within the voice or data communication of a question or instruction voiced by applying an acoustic model for tagging identified parts about the question or instruction voiced with query parameters in response to the pilot.