Abstract: Disclosed is a speech recognition method of an artificial intelligence robot. The speech recognition method includes: receiving uttered speech information of a user from an external device; inputting the speech information to a pre-learned first intent analysis model, and determining an utterance intent of the user according to an output value of the first intent analysis; transmitting response information corresponding to the determined utterance intent of the user to the external device; receive evaluation information of the user on the response information from the external device; and generating a second intent analysis model by adding the evaluation information to learning data and learning the first intent analysis model. Accordingly, an intelligent device is capable of learning an accurate utterance intent even without error correction by a manager.

Abstract: A message management unit receives and accumulates a message, wherein the message is distributed for every update, is the message data representing a latest situation of a competition, an explanation generation unit generates an explanatory text for conveying unconveyed information detected from the message, based on conveyed information, a speech synthesis unit outputs a speech converted from the explanatory text, wherein the explanation generation unit stores the unconveyed information for the explanatory text as the conveyed information, stands by until completion of completion of the speech, and initiates a procedure for generating a new explanatory text based on updated unconveyed information.

Abstract: Recommending an automated assistant action for inclusion in an existing automated assistant routine of a user, where the existing automated assistant routine includes a plurality of preexisting automated assistant actions. If the user confirms the recommendation through affirmative user interface input, the automated assistant action can be automatically added to the existing automated assistant routine. Thereafter, when the automated assistant routine is initialized, the preexisting automated assistant actions of the routine will be performed, as well as the automated assistant action that was automatically added to the routine in response to affirmative user interface input received in response to the recommendation.

Abstract: Implementations relate to determining a language for speech recognition of a spoken utterance, received via an automated assistant interface, for interacting with an automated assistant. In various implementations, audio data indicative of a voice input that includes a natural language request from a user may be applied as input across multiple speech-to-text (“STT”) machine learning models to generate multiple candidate speech recognition outputs. Each STT machine learning model may trained in a particular language. For each respective STT machine learning model of the multiple STT models, the multiple candidate speech recognition outputs may be analyzed to determine an entropy score for the respective STT machine learning model. Based on the entropy scores, a target language associated with at least one STT machine learning model of the multiple STT machine learning models may be selected. The automated assistant may respond to the request using the target language.

Abstract: A word extraction method according to at least one embodiment of the present disclosure includes: converting, with at least one processor operating with a memory device in a device, received speech information into text data; converting the text data into a string of words including a plurality of words; extracting, with the at least one processor operating with the memory device in the device, a keyword included in a keyword database from the plurality of words; and calculating, with the at least one processor operating with the memory device in the device, importance levels of the plurality of words based on timing of utterance of the keyword and timing of utterance of each of the plurality of words.

Abstract: An electronic apparatus and a method for controlling thereof are provided. The electronic apparatus includes a microphone configured to receive a user voice, a communication interface, and a processor configured, based on a first voice being received through the microphone, to provide first response information corresponding to the first voice, and based on a user sensing signal being received from an external apparatus through the communication interface, to control the communication interface to send the first response information to the external apparatus.

Abstract: The present disclosure provides a translation processing method, a translation processing device, and a device. The first speech signal of the first language is obtained, and the speech feature vector of the first speech signal is extracted based on the preset algorithm. Further, the speech feature vector is input into the pre-trained end-to-end translation model for conversion from the first language speech to the second language text for processing, and the text information of the second language corresponding to the first speech signal is obtained. Moreover, speech synthesis is performed on the text information of the second language, and the corresponding second speech signal is obtained and played.

Abstract: Implementations set forth herein relate to phasing-out of vehicle computing device versions while ensuring useful responsiveness of any vehicle computing device versions that are still in operation. Certain features of updated computing devices may not be available to prior versions of computing devices because of hardware limitations. The implementations set forth herein eliminate crashes and wasteful data transmissions caused by prior versions of computing devices that have not been, or cannot be, upgraded. A server device can be responsive to a particular intent request provided to a vehicle computing device, despite the intent request being associated with an action that a particular version of the vehicle computing device cannot execute. In response, the server device can elect to provide speech to text data, and/or natural language understanding data, in furtherance of allowing the vehicle computing device to continue leveraging resources at the server device.

Abstract: Provided are an electronic device for recognizing speech of a user, and a method, performed by the electronic device, of recognizing speech. The method includes obtaining an audio signal based on a speech input based on the audio signal being input, obtaining an output value of a first automatic speech recognition (ASR) model that outputs a character string at a first level; obtaining an output value of a second ASR model that outputs a character string at a second level corresponding to the audio signal based on the output value of the first ASR model based on the audio signal being input; and recognizing the speech from the output value of the second ASR model.

Abstract: The present disclosure relates to a system, a method, and a product for an artificial intelligence based virtual agent trainer. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to obtain input data and generate a preliminary set of utterances based on the input data, process the preliminary set of utterances to generate a set of utterance training data, generate a set of conversations based on the set of utterance training data, simulate the set of conversations on a virtual agent to obtain a conversation result, verify an intent and a response based on the conversation result, verify a use case flow and flow hops based on the conversation result, and generate recommendation information and maturity report based on verification results.

Abstract: Embodiments of the present invention provide a computer-implemented method for cognitive collation configuration processing of multilingual data. The method includes parsing a multilingual input text into a plurality of collation items. The method includes detecting a language of each collation item of the plurality of collation items. The method includes storing each collation item, of the plurality of collation items, into a corresponding sub language buffer of a plurality of sub language buffers. The method includes performing a first sort operation on the plurality of sub language buffers, in which the first sort operation includes sorting the plurality of sub language buffers based on a set of collation settings, in which the set of collation settings includes a language selection list. The method includes merging the content of the sorted plurality of sub language buffers to form a sorted output comprising the plurality of collation items.

Abstract: Example speech enhancement systems include a spatio-temporal residual network configured to receive video data containing a target speaker and extract visual features from the video data, an autoencoder configured to receive input of an audio spectrogram and extract audio features from the audio spectrogram, and a squeeze-excitation fusion block configured to receive input of visual features from a layer of the spatio-temporal residual network and input of audio features from a layer of the autoencoder, and to provide an output to the decoder of the autoencoder. The decoder is configured to output a mask configured based upon the fusion of audio features and visual features by the squeeze-excitation fusion block, and the instructions are executable to apply the mask to the audio spectrogram to generate an enhanced magnitude spectrogram, and to reconstruct an enhanced waveform from the enhanced magnitude spectrogram.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, in an existing human-to-computer dialog session between a user and an automated assistant, it may be determined that the automated assistant has responded to all natural language input received from the user. Based on characteristic(s) of the user, information of potential interest to the user or action(s) of potential interest to the user may be identified. Unsolicited content indicative of the information of potential interest to the user or the action(s) may be generated and incorporated by the automated assistant into the existing human-to-computer dialog session.

Abstract: Generating and/or recommending command bundles for a user of an automated assistant. A command bundle comprises a plurality of discrete actions that can be performed by an automated assistant. One or more of the actions of a command bundle can cause transmission of a corresponding command and/or other data to one or more devices and/or agents that are distinct from devices and/or agents to which data is transmitted based on other action(s) of the bundle. Implementations determine command bundles that are likely relevant to a user, and present those command bundles as suggestions to the user. In some of those implementations, a machine learning model is utilized to generate a user action embedding for the user, and a command bundle embedding for each of a plurality of command bundles. Command bundle(s) can be selected for suggestion based on comparison of the user action embedding and the command bundle embeddings.

Abstract: Provided are an apparatus and a method, a variety of embodiments of the apparatus comprising a microphone, memory, and a processor functionally connected to the microphone or memory, wherein the processor is configured to: count end-point detection (EPD) time on the basis of a voice input; when the EPD time expires, determine whether the final word of the voice input corresponds to a previously configured word stored in memory; and, if the final word corresponds to the previously configured word, then extend the EPD time and wait for reception of a voice input. Additionally, other embodiments are possible.

Abstract: Embodiments for managing a chatbot by one or more processors are described. A communication from an individual is received. At least one data source associated with the individual is selected based on the received communication. A response to the received communication is generated based on the at least one selected data source.

Abstract: Methods for detecting whether a rendered version of a specified seamless connection (“SSC”) at a connection point between two audio segment sequences results in an audible discontinuity, and methods for analyzing at least one SSC between audio segment sequences to determine whether a renderable version of each SSC would have an audible discontinuity at the connection point when rendered, and in appropriate cases, for a SSC having a renderable version which is determined to have an audible discontinuity when rendered, correcting at least one audio segment of at least one segment sequence to be connected in accordance with the SSC in an effort to ensure that rendering of the SSC will result in seamless connection without an audible discontinuity. Other aspects are editing systems configured to implement any of the methods, and storage media and rendering systems which store audio data generated in accordance with any of the methods.

Abstract: Embodiments of the present systems and methods may provide techniques by which bots may be analyzed using improved representations of bot structure and a means of assessing conversational quality that may provide improved efficiency. For example a method may comprise training, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, a neural network model to learn representations that capture characteristics of the graphs of chatbots, wherein the captured characteristics include at least a content-based representation based on user utterances that are relevant to the nodes and based on the chatbot response for the nodes.

Abstract: Disclosed herein are an apparatus and method for recognizing a voice speaker. The apparatus for recognizing a voice speaker includes a voice feature extraction unit configured to extract a feature vector from a voice signal inputted through a microphone; and a speaker recognition unit configured to calculate a speaker recognition score by selecting a reverberant environment from multiple reverberant environment learning data sets based on the feature vector extracted by the voice feature extraction unit and to recognize a speaker by assigning a weight depending on the selected reverberant environment to the speaker recognition score.

Abstract: Disclosed is a system for language-independent contextual embedding of entities in a document that includes sentences. The system has a database and a processing arrangement. The processing arrangement has a tokenizer module for tokenizing sentences to obtain tokens, an encoder module for determining character coordinate corresponding to the tokens, wherein the character coordinates corresponding to the tokens occur in a multi-dimensional hierarchical space. The system has a transmutation module for processing the character coordinates to generate contextual embeddings thereof in the multi-dimensional hierarchical space and a prediction module for memorizing sequential information pertaining to the contextual embeddings of the character coordinates.