Abstract: A computing system obtains text that includes words and provides the text as input to an emotional classifier model that has been trained based upon emotional classification. The computing system obtains a textual embedding of the computer-readable text as output of the emotional classifier model. The computing system generates a phoneme sequence based upon the words of the text. The computing system, generates, by way of an encoder of a text to speech (TTS) model, a phoneme encoding based upon the phoneme sequence. The computing system provides the textual embedding and the phoneme encoding as input to a decoder of the TTS model. The computing system causes speech that includes the words to be played over a speaker based upon output of the decoder of the TTS model, where the speech reflects an emotion underlying the text due to the textual embedding provided to the encoder.

Abstract: Frequent sequences extracted from a set of documents according to a common rule are obtained. Based on comparing occurrence frequencies of various sequences, confidence of the first frequent sequence being a label expression representing a document part in a target document is evaluated. Keywords are extracted from the target document based on evaluation of the confidence.

Abstract: Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.

Abstract: A speech module is joint trained with a knowledge module by transforming a first knowledge graph into an acoustic knowledge graph. The knowledge module is trained on the acoustic knowledge graph. Then, the knowledge module is integrated with the speech module to generate an integrated knowledge-speech module. In some instances, the speech module included in the integrated knowledge-speech module is aligned with a language module to generate an optimized speech model configured to leverage acoustic information and acoustic-based knowledge information, along with language information.

Abstract: A method for determining a category responsive to a user query is disclosed. The method includes receiving a training data set comprising a plurality of data pairs, each data pair including: (i) a query; and (ii) an associated one or more categories that are responsive to the query, wherein the one or more categories in the training data set defines a plurality of categories. The method includes training a machine learning algorithm, according to the training data set, to create a trained model, wherein training the machine learning algorithm includes: creating a first co-occurrence data structure defining co-occurrence of respective word representations of the queries with the plurality of categories, and creating a second co-occurrence data structure defining co-occurrence of respective categories in respective data pairs. The method also includes deploying the trained model to return one or more categories in response to a new query input.

Abstract: A computer-implemented method for counterfactual conversation simulation is disclosed. The computer-implemented method includes generating a system output based, at least in part, on a user input. The computer-implemented method further includes determining that a system output/user input pair is not satisfactory based, at least in part, on a system output/user input score being below a predetermined threshold. The computer-implemented method further includes generating, in response to determining the system output/user input pair is not satisfactory, a counterfactual simulation of the user input based, at least in part, on a target intent of the user input.

Abstract: Systems and methods are described for providing subtitles for a media content item. Subtitles are obtained, using control circuitry, for the media content item. Control circuitry determines whether a character component of the subtitles should be replaced by an image component. In response to determining that the character component of the subtitles should be replaced by an image component, control circuitry selects, from memory, an image component corresponding to the character component. Control circuitry replaces the character component of the subtitles by the image component to generate modified subtitles.

Abstract: Disclosed is an electronic device including a communication interface, a memory, a microphone, a speaker, a display, a main processor, and a sub-processor activating the main processor by recognizing a wake-up word included in a voice input. The at least one memory stores instructions that, when executed, cause the main processor to receive a first voice input to register the wake-up word, when the first voice input does not include a specified word, to receive a second voice input including a word identical to the first voice input, through the microphone, to generate a wake-up word recognition model for recognizing the wake-up word, and to store the generated wake-up word recognition model in the at least one memory, and when the first voice input includes the specified word, to output information for requesting a third voice input, through the speaker or the display.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: Techniques for conversational-based searching are described. A system may receive a first spoken user input, and may determine that the first spoken user input corresponds to a request for information associated with an entity. The system may retrieve item results corresponding to the entity. The system may determine a suggested user input based on the retrieved item results, and may determine output data corresponding to the suggested user input. The system may send output data to a user device, where the output data includes the item results and the suggested user input. The system may receive a second spoken user input, and may determine that the second spoken user input corresponds to the suggested user input. In response, the system may send the previously determined output data to the device.

Abstract: Implementations relate to effectively localizing system responses, that include dynamic information, to target language(s), such that the system responses are grammatical and/or natural in the target language(s). Some of those implementations relate to various techniques for resource efficient generation of templates for a target language. Some versions of those implementations relate to resource efficient generation of target language natural language generation (NLG) templates and, more particularly, to techniques that enable a human user to generate a target language NLG template more efficiently and/or with greater accuracy. The more efficient target language NLG template generation enables less utilization of various client device resources and/or can mitigate the risk of flawed NLG templates being provided for live use in one or more systems.

Abstract: A method in an interactive computing-system includes pre-processing an input natural-language (NL) from a user command based on natural language processing (NLP) for classifying speech information and non-speech information, obtaining an NLP result from the user command, fetching a device specific information from one or more IoT devices operating in an environment based on the NLP result, generating one or more contextual parameters based on the NLP result and the device specific information, selecting at least one speaker embedding stored in a database for the one or more IoT devices based on the one or more contextual parameters, and outputting the selected at least one speaker embedding for playback to the user.

Abstract: There is inter alia disclosed an apparatus for spatial audio encoding which can receive or determine for one or more audio signals (102), spatial audio parameters (106) on a sub band basis for providing spatial audio reproduction, the spatial audio parameters can comprise a coherence value (112) for each sub band of a plurality of subbands (202) of a frame. The apparatus then determines a significance measure for the coherence values (401) of the plurality of sub bands of the frame and uses the significance measure to determine whether to encode (403) the coherence values of the plurality of sub bands of the frame.

Abstract: A system and method code an object-based audio signal comprising audio objects in response to audio streams with associated metadata. In the system and method, a metadata processor codes the metadata and generates information about bit-budgets for the coding of the metadata of the audio objects. An encoder codes the audio streams while a bit-budget allocator is responsive to the information about the bit-budgets for the coding of the metadata of the audio objects from the metadata processor to allocate bitrates for the coding of the audio streams by the encoder.

Abstract: A system and method for automatically generating organization level ontology for knowledge retrieval, are provided. An input/output unit receives a plurality of documents from document sources and an ontology generation system generates the organization level ontology based on the documents. The ontology generation system extracts one or more nodes and directed relationships from each document and generates an intermediate document ontology for each document. A combination of syntactic, semantic, and pragmatic assessment of intermediate document ontology is performed to assess at least structure and adaptability of the ontology. The ontology generation system further generates a refined document ontology, based on assessment, to satisfy one or more quality metrics. Each of the refined document ontologies is integrated together to generate the organization level ontology.

Abstract: A computing device receives, in a graphical user interface, a first natural language query. The computing device parses the first natural language query, thereby identifying one or more keywords in the query. The computing device identifies one or more data sources that are relevant to the query. The computing device also identifies one or more data fields and/or data values from the data sources in the query. The computing device compares the keywords to respective trigger text for each of a plurality of data analysis templates, thereby selecting a first data analysis template. The first data analysis template includes a plurality of predefined data visualizations. The computing device generates a dashboard that includes the data visualizations using the identified data fields and/or data values, and displays the dashboard in the graphical user interface.

Abstract: A computer-implemented method for measuring cognitive load of a user creating a creative work in a creative work system, may include generating at least one verbal statement capable of provoking at least one verbal response from the user, prompting the user to vocally interact with the creative work system by vocalizing the at least one generated verbal statement to the user via an audio interface of the creative work system, and obtaining the at least one verbal response from the user via the audio interface, and determining the cognitive load of the user based on the at least one verbal response obtained from the user, wherein generating the at least one verbal statement is based on at least one predicted verbal response suitable for determining the cognitive load of the user.

Abstract: Disclosed embodiments may include a system that may receive an audio file comprising an interaction between a first user and a second user. The system may detect, using a deep neural network (DNN), moment(s) of interruption between the first and second users from the audio file. The system may extract, using the DNN, vocal feature(s) from the moment(s) of interruption. The system may determine, using a machine learning model (MLM) and based on the vocal feature(s), whether a threshold number of moments of the moment(s) of interruption corresponds to a first emotion type. When the threshold number of moments corresponds to the first emotion type, the system may transmit a first message comprising a first binary indication. When the threshold number of moments do not correspond to the first emotion type, the system may transmit a second message comprising a second binary indication.

Abstract: A computer system extracts clauses using machine translation. An input sentence in a source language is translated into a translated sentence in a target language using a trained machine translation model, wherein the trained machine translation model inserts a grammatical indicator into a position of the translated sentence that identifies a dependent clause. The input sentence and the translated sentence are aligned to determine a position in the input sentence that corresponds to the position of the grammatical indicator in the translated sentence. The dependent clause is extracted, in the source language, from the input sentence based on the determined position in the input sentence. Embodiments of the present invention further include a method and program product for clause extraction using machine translation in substantially the same manner described above.

Abstract: Disclosed is an electronic apparatus. The electronic apparatus includes: a communication interface, a memory, and a processor connected to the memory and the communication interface, the processor configured to control the electronic apparatus to, based on receiving a speech related to a function of the electronic apparatus, obtain text information corresponding to the received speech, control the communication interface to transmit the obtained text information to a server including a first neural network model corresponding to the function, execute the function based on response information received from the server, and based on identifying that an update period of the first neural network model is greater than or equal to a first threshold period based on the information related to the function of the electronic apparatus, the electronic apparatus may receive the information about the first neural network model from the server and store the information in the memory.