Abstract: A multi-lingual speech recognition and theme-semanteme analysis method comprises steps executed by a speech recognizer: obtaining an alphabet string corresponding to a voice input signal according to a pronunciation-alphabet table, determining that the alphabet string corresponds to original words according to a multi-lingual vocabulary, and forming a sentence according to the multi-lingual vocabulary and the original words, and comprises steps executed by a sematic analyzer: according to the sentence and a theme vocabulary-semantic relationship data set, selectively executing a correction procedure to generate a corrected sentence, an analysis state determining procedure or a procedure of outputting the sentence, outputting the corrected sentence when the correction procedure successes, and executing the analysis state determining procedure to selectively output a determined result when the correction procedure fails.

Abstract: There is provided an information processing device and an information processing method that enable the intention of a speech of a user to be estimated more accurately. The information processing device includes: a detection unit configured to detect a breakpoint of a speech of a user on the basis of a result of recognition that is to be obtained during the speech of the user; and an estimation unit configured to estimate an intention of the speech of the user on the basis of a result of semantic analysis of a divided speech sentence obtained by dividing a speech sentence at the detected breakpoint of the speech. The present technology can be applied, for example, to a speech dialogue system.

Abstract: An electronic device and a controlling method of the electronic device are provided. The electronic device acquires text to respond on a received user's speech, acquires a plurality of pieces of parameter information for determining a style of an output speech corresponding to the text based on information on a type of a plurality of text-to-speech (TTS) databases and the received user's speech, identifies a TTS database corresponding to the plurality of pieces of parameter information among the plurality of TTS databases, identifies a weight set corresponding to the plurality of pieces of parameter information among a plurality of weight sets acquired through a trained artificial intelligence model, adjusts information on the output speech stored in the TTS database based on the weight set, synthesizes the output speech based on the adjusted information on the output speech, and outputs the output speech corresponding to the text.

Abstract: Provided are techniques for domain specific correction of output from automatic speech recognition. An output of an automatic speech recognition engine is received. An alphanumeric sequence is extracted from the output, where the alphanumeric sequence represents an erroneous translation by the automatic speech recognition engine. Candidates for the alphanumeric sequence are generated. The candidates are ranked based on scores associated with the candidates. A candidate of the candidates having a highest score of the scores is selected. The output is corrected by replacing the alphanumeric sequence with the selected candidate. The corrected output is returned.

Abstract: A portable radio (100) provides a voice control feature which advantageously enables radio users to perform talk group changes by using voice as an input medium to the radio device. Activation of a voice control button (110) and verbal entry of a valid talk group control command enables a change in talk group. The portable radio (100) comprises a controller (102) having a code plug (120) pre-programmed with a plurality of talk group alias names, and an automatic speech recognition (ASR) engine (118) providing voice control entry detection to the portable radio. The controller (102) enables a change in talk group in response to the ASR engine (118) detecting that the voice control entry is a valid talk group control command. Shortened audible confirmations of talk group change are generated using the alias names.

Abstract: Techniques are described herein for chatbots to achieve greater social grace by tracking users' states and providing corresponding dialog. In various implementations, input may be received from a user at a client device operating a chatbot, e.g., during a first session between the user and the chatbot. The input may be semantically processed to determine a state expressed by the user to the chatbot. An indication of the state expressed by the user may be stored in memory for future use by the chatbot. It may then be determined, e.g., by the chatbot based on various signals, that a second session between the user and the chatbot is underway. In various implementations, as part of the second session, the chatbot may output a statement formed from a plurality of candidate words, phrases, and/or statements based on the stored indication of the state expressed by the user.

Abstract: An electronic device and an method of the electronic device are provided, where the electronic device maintains a context that does not reflect a request for a secret conversation, in response to the request for the secret conversation being received from a first user, and generates a response signal to a voice signal of a second user based on the maintained context, in response to an end of the secret conversation with the first user.

Abstract: An applicant can instantiate a parsing framework, provide an input stream, attach observers, and initiate parsing, which inverts control to the parsing framework. The parsing framework can have an observer manager, a parser controller, and parsers. The observer manager manages observer design patterns from which the observers are instantiated. The parser controller determines which parser would be appropriate for parsing the input stream and instantiate the appropriate parser(s). The parser controller gets the callbacks from the parsers and communicates outcomes to the observer manager. The observer manager determines which of the observers is to be notified, generates parsing notifications accordingly, and dispatches the parsing notifications directly to the observers. The application can be any application that needs parsing in an electronic information exchange platform.

Abstract: A beacon system is used to monitor the location and identity of individuals using stationary sniffers and to provide that information to a virtual assistant for improved functionality and better context derived from location with respect to standard virtual assistant queries. The system provides a heuristic mapping process providing increasingly precise mapping of the individual with minimal consumer input.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for identifying a key phrase in audio, a device and a computer readable storage medium. The method for identifying a key phrase in audio includes obtaining audio data to be identified. The method further includes identifying the key phrase in the audio data using a trained key phrase identification model. The key phrase identification model is trained based on first training data for identifying feature information of words in a first training text and second training data for identifying the key phrase in a second training text. In this way, embodiments of the present disclosure can accurately and efficiently identify key information in the audio data.

Abstract: Messages are processed to generate effectiveness predictions and/or other insights associated with the messages. Candidate messages are processed through a natural language processing (NLP) component to parse the candidate message into message elements for further processing. The message elements are converted to a vector or set of vectors, which are provided as input to a machine learning model to make predictions of message effectiveness. A contribution score can be made for each message element of the candidate message, which may be indicative of the importance or relevance for the individual message element to the overall predicted message effectiveness. Other message elements not originally within the message can be provided as candidates to replace message elements already located within the message. In this way, a message that is likely to be effective, such being likely to have a high conversion rate, can be published or otherwise distributed.

Abstract: Disclosed are a speech processing method and apparatus therefor which execute an installed artificial intelligence algorithm and/or machine learning algorithm to perform speech processing in a 5G communication environment.

Abstract: [Object] To provide a reproduction apparatus and a reproduction method which enable easier operation of providing a response to distributed information. [Solution] A reproduction terminal including: a reproducing unit configured to reproduce distributed information; a sensor unit configured to detect body action of a user; and a control unit configured to process operation regarding provision of a response to the distributed information on the basis of the body action detected at the sensor unit.

Abstract: In a voice output apparatus, an acquisition unit acquires a speech of an occupant of a vehicle. A determination unit determines whether or not the acquired speech is asking for repetition or rephrasing. A classification unit classifies, when it is determined that the speech is the asking, the speech according to a type of asking. An output unit outputs a voice sound in accordance with the classified type of the asking based on a content of a voice sound that is a target of the asking.

Abstract: In order to improve the accuracy of ASR, an utterance is transcribed using a plurality of language models, such as for example, an N-gram language model and a neural language model. The language models are trained separately. They each output a probability score or other figure of merit for a partial transcription hypothesis. Model scores are interpolated to determine a hybrid score. While recognizing an utterance, interpolation weights are chosen or updated dynamically, in the specific context of processing. The weights are based on dynamic variables associated with the utterance, the partial transcription hypothesis, or other aspects of context.

Abstract: A non-transitory computer-readable storage medium storing a program that causes a processor included in a computer mounted on a sound source direction estimation device to execute a process, the process includes calculating a sound pressure difference between a first voice data acquired from a first microphone and a second voice data acquired from a second microphone and estimating a sound source direction of the first voice data and the second voice data based on the sound pressure difference, outputting an instruction to execute a voice recognition on the first voice data or the second voice data in a language corresponding to the estimated sound source direction, and controlling a reference for estimating a sound source direction based on the sound pressure difference, based on a time length of the voice data used for the voice recognition based on the instruction and a voice recognition time length.

Abstract: A system and method for dictation using a peripheral device includes a voice recognition mouse. The voice recognition mouse includes a microphone, a first button, a processor coupled to the microphone and the first button, and a memory coupled to the processor. The memory stores instructions that, when executed by the processor, cause the processor to detect actuation of the first button and in response to detecting actuation of the first button, invoke the microphone for capturing audio speech from a user. The captured audio speech is streamed to a first module. The first module is configured to invoke a second module for converting the captured audio speech into text and forward the text to the first module for providing to an application expecting the text, the application being configured to display the text on a display device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance. The actions further include determining that the utterance likely includes a particular, predefined hotword. The actions further include transmitting (i) data indicating that the computing device likely received the particular, predefined hotword, (ii) data identifying the computing device, and (iii) data identifying a group of nearby computing devices that includes the computing device. The actions further include receiving an instruction to commence speech recognition processing on the audio data. The actions further include in response to receiving the instruction to commence speech recognition processing on the audio data, processing at least a portion of the audio data using an automated speech recognizer on the computing device.

Abstract: Aspects of the invention include acoustic signature generation via user sentiment embedding. An example method includes an audio signal of user speech, obtaining a user context and a user state space for at least one user, generating a sentiment vector from the user context and the user state space, generating a set of sentiment signals for based on the sentiment vector, and embedding the audio signal with the set of sentiment signals to generate an embedded sentiment signal.

Abstract: A method for generating a human-like response to a voice or text command includes receiving an input sequence of words and processing the input sequence of words to generate a trigger word that is indicative of a desired nature of the human-like response. The method further includes encoding a neural network using the trigger word and generating the human-like response using an output of the neural network. The method enables implementation of voice command functionality in various types of devices with only a small amount of training data.