Abstract: Provided is a method of automatically expanding input text. The method includes receiving input text composed of a plurality of documents, extracting a sentence pair that is present in different documents among the plurality of documents, setting the extracted sentence pair as an input of an encoder of a sequence-to-sequence model, setting an output of the encoder as an output of a decoder of the sequence-to-sequence model and generating a sentence corresponding to the input, and generating expanded text based on the generated sentence.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: A method, system, and a computer-readable recording medium for improving a set of at least one semantic unit are provided. According to the present invention, a set of at least one semantic unit may be improved by using a phonetic sound or text.

Abstract: Identification of keywords from media content metadata including caption data is provided. When a piece of media content is received by a user, media content metadata and caption data may be provided to a data mining engine operable to identify and tag keywords. Identified keyword data may be provided to one or more native or third party applications for providing information to the user relevant to what he is watching.

Abstract: A technology of accurately coding and decoding coefficients which are convertible into linear prediction coefficients even for a frame in which the spectrum variation is great while suppressing an increase in the code amount as a whole is provided. A coding device includes: a first coding unit that obtains a first code by coding coefficients which are convertible into linear prediction coefficients of more than one order; and a second coding unit that obtains a second code by coding at least quantization errors of the first coding unit if (A-1) an index Q commensurate with how high the peak-to-valley height of a spectral envelope is, the spectral envelope corresponding to the coefficients which are convertible into the linear prediction coefficients of more than one order, is larger than or equal to a predetermined threshold value Th1 and/or (B-1) an index Q? commensurate with how short the peak-to-valley height of the spectral envelope is, is smaller than or equal to a predetermined threshold value Th1?.

Abstract: The disclosed subject matter provides a system, computer readable storage medium, and a method providing an audio and textual transcript of a communication. A conferencing services may receive audio or audio visual signals from a plurality of different devices that receive voice communications from participants in a communication, such as a chat or teleconference. The audio signals representing voice (speech) communications input into respective different devices by the participants. A translation services server may receive over a separate communication channel the audio signals for translation into a second language. As managed by the translation services server, the audio signals may be converted into textual data. The textual data may be translated into text of different languages based the language preferences of the end user devices in the teleconference. The translated text may be further translated into audio signals.

Abstract: Embodiments are directed to a spellcheck module for an enterprise search engine. The spellcheck module includes a candidate suggestion generation module that generates a number of candidate words that may be the correction of the misspelled word. The candidate suggestion generation module implements an algorithm for indexing, searching, and storing terms from an index with a constrained edit distance, using words in a collection of documents. The spellcheck module further includes a candidate suggestion ranking module. In one embodiment, a non-contextual approach using a linear combination of distance and probability scores is utilized; while in another embodiment, a context sensitive approach accounting for real-word misspells and adopting deep learning models is utilized. In use, a query is provided to the spellcheck module to generate results in the form of a ranked list of generated candidate entries that may be an entry a user accidentally misspelled.

Abstract: An electronic device is provided including at least one microphone, a communication circuit, a processor and a memory, wherein the memory stores at least one application program or a software program executing a voice instruction, which is triggered in response to a voice input, upon the performance, the memory stores instructions to allow the processor to sequentially receive a plurality of utterances including a first speech element from a first user through the at least one microphone, generate a voice recognition model of the first user on the basis of at least some of the plurality of utterances, store the generated voice recognition model in the memory, and transmit the generated voice recognition model of the first user to the outside through the communication circuit so that a first external device uses the generated voice recognition model of the first user.

Abstract: The present invention relates to a method, an apparatus, and a computer-readable recording medium for improving at least one semantic unit set by using a phonetic sound. The method for improving a set including at least one semantic unit, in which the set including at least one semantic unit is a captured semantic unit set, includes: receiving an improvement phonetic sound according to utterance of a user; specifying an improvement semantic unit set on the basis of the improvement phonetic sound; specifying a semantic unit set as a target to be actually improved within the captured semantic unit set, as a matched semantic unit set, on the basis of correlation thereof with the improvement semantic unit set; and replacing the matched semantic unit set within the captured semantic unit set with the improvement semantic unit set.

Abstract: The disclosed embodiments relate to the design of a system that uses an accelerometer in a mobile device to detect hotwords, which activate a voice interface to recognize subsequent voice input. During operation, the system gathers samples comprising readings from the accelerometer in the mobile device. Next, the system calculates features from the gathered samples. The system then classifies the calculated features using a classifier, which has been trained to detect hotwords based on calculated features. Finally, if the classifier detects one or more hotwords, the system causes the mobile device to launch a voice-control system that recognizes subsequent voice input received from an audio microphone.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a providing automated natural dialog with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialog management device, an API server, and an NLP device. The dialog management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained-machine learning model.

Abstract: The present specification discloses various embodiments of a digital device and a method for controlling the same. A method for controlling a digital device according to an embodiment of the present invention may comprise the steps of: connecting to a repeater; sensing an external device located within a preconfigured distance and connected to the repeater; receiving an audio reference data transmission request from the sensed external device; and transmitting the requested audio reference data to the external device.

Abstract: Systems and methods are described herein for providing media guidance. Control circuitry may receive a first voice input and access a database of topics to identify a first topic associated with the first voice input. A user interface may generate a first response to the first voice input, and subsequent to generating the first response, the control circuitry may receive a second voice input. The control circuitry may determine a match between the second voice input and an interruption input such as a period of silence or a keyword or a phrase, such as “Ahh,”, “Umm,”, or “Hmm.” The user interface may generate a second response that is associated with a second topic related to the first topic. By interrupting the conversation and changing the subject from time to time, media guidance systems can appear to be more intelligent and human.

Abstract: According to an embodiment, a conference support system includes a recognizer, a classifier, a first caption controller, a second caption controller, and a display controller. The recognizer is configured to recognize text data corresponding speech from a speech section and configured to distinguish between the speech section and a non-speech section in speech data. The classifier is configured to classify the text data into first utterance data representing a principal utterance and second utterance data representing another utterance. The first caption controller is configured to generate first caption data for displaying the first utterance data without waiting for identification of the first utterance data to finish. The second caption controller is configured to generate second caption data for displaying the second utterance data after identification of the second utterance data finishes. The display controller is configured to control a display of the first caption data and the second caption data.

Abstract: A wearable utterance training system includes a wearable utterance training device. The system may, for example: (1) receive one or more target utterances from the user; (2) detect a use of one of the one or more target utterances by the user; and (3) in response, provide one or more responsive effects. The one or more responsive effects may include, for example: (1) providing one or more shocks to the user using the wearable utterance training device; (2) initiating a transfer of money between an account associated with the user and a third party account; (3) creating a public disclosure of the utterance (e.g., by posting the disclosure on one or more social media websites) and/or (4) playing a recording of the user's use of the target utterance or other sound.

Abstract: Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, where the method includes determining an input audio signal as a to-be-determined audio signal; determining an enhanced segmental signal-to-noise ratio (SSNR) of the audio signal, where the enhanced SSNR is greater than a reference SSNR; and comparing the enhanced SSNR with a voice activity detection (VAD) decision threshold to determine whether the audio signal is an active signal. According to the method and the apparatus provided in the embodiments, an active voice and an inactive voice can be accurately distinguished.

Abstract: According to an embodiment, a speech section detection device includes a reception unit and a detection unit. The reception unit is configured to receive, from an external device, a first voice signal that is a signal in which likelihood indicating a probability of speech is equal to or more than a first threshold. The detection unit is configured to detect, from the first voice signal, a second voice signal that is a signal of a section in which the likelihood is equal to or more than a second threshold that is larger than the first threshold.

Abstract: An embodiment provides a method, including: receiving, at a device, user input; identifying, using a processor, elements included in the user input; determining, using a processor, that at least one of the identified elements renders the user input ambiguous; identifying, using a processor, a source of context data; accessing, using a processor, context data associated with the user input from the source of context data; disambiguating, using a processor, the user input based on the context data associated with the user input; and forming, using a processor, an altered input based on the disambiguating. Other embodiments are described and claimed.

Abstract: A correction system of the embodiment includes an interface system, a calculator, a generator, and a display controller. The interface system receives correction information for correcting a voice recognition result. The calculator estimates a part of the voice recognition result to be corrected and calculates a degree of association between the part to be corrected and the correction information. The generator generates corrected display information comprising at least one of the correction information and the part to be corrected using a display format corresponding to the degree of association. The display controller outputs the corrected display information on a display.

Abstract: A sign language recognizer is configured to detect interest points in an extracted sign language feature, wherein the interest points are localized in space and time in each image acquired from a plurality of frames of a sign language video; apply a filter to determine one or more extrema of a central region of the interest points; associate features with each interest point using a neighboring pixel function; cluster a group of extracted sign language features from the images based on a similarity between the extracted sign language features; represent each image by a histogram of visual words corresponding to the respective image to generate a code book; train a classifier to classify each extracted sign language feature using the code book; detect a posture in each frame of the sign language video using the trained classifier; and construct a sign gesture based on the detected postures.