Abstract: A method includes receiving audio data corresponding to an utterance spoken by a user and processing, using a first recognition model, the audio data to generate a non-contextual candidate hypothesis as output from the first recognition model. The non-contextual candidate hypothesis has a corresponding likelihood score assigned by the first recognition model. The method also includes generating, using a second recognition model configured to receive personal context information, a contextual candidate hypothesis that includes a personal named entity. The method also includes scoring, based on the personal context information and the corresponding likelihood score assigned to the non-contextual candidate hypothesis, the contextual candidate hypothesis relative to the non-contextual candidate hypotheses.

Abstract: A data processing method is described. The method includes acquiring a to-be-filtered dataset, the to-be-filtered dataset including a plurality of pieces of to-be-filtered source language data; filtering all source language data in the to-be-filtered dataset based on a target data filtering model to obtain target source language data remaining after the filtering, the target data filtering model being obtained through training performed by using a reinforcement learning algorithm; and acquiring markup language data corresponding to the obtained target source language data, and acquiring a machine translation model based on the target source language data and the acquired markup language data. In such a data processing process, a filtering rule in the target data filtering model is automatically learned by a machine in a reinforcement learning process. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Abstract: A chatbot system is configured to execute code to perform determining, by the chatbot system, a classification result for an utterance and one or more anchors each anchor of the one or more anchors corresponding to one or more anchor words of the utterance. For each anchor of the one or more anchors, one or more synthetic utterances are generated, and one or more classification results for the one or more synthetic utterances are determined. A report is generated by the chatbot system comprising a representation of a particular anchor of the one or more anchors, the particular anchor corresponding to a highest confidence value among the one or more anchors. The one or more synthetic utterances may be used to generate a new training dataset for training a machine-learning model. The training dataset may be refined according to a threshold confidence values to filter out datasets for training.

Abstract: An audio signal encoding method and apparatus, and an audio signal decoding method and apparatus are disclosed.

Abstract: A technique is described for performing audio-based patient surveys to obtain patient data for use in a health management platform. A virtual assistant is simulated to participate in a natural language conversation with a patient. As part of the conversation, the virtual assistant generates and outputs an audible natural language message that is then presented via a speaker at a user computing device. The audible natural language message includes a prompt for the user to provide a natural language reply. Reply data indicative of a reply by the user is then received and processed to generate and/or update patient data associated with the patient. The patient data is then applied by the health management platform to assist the patient in managing a chronic health condition.

Abstract: This application discloses an audio signal processing method performed by an electronic device. According to this application, embedding processing is performed on a mixed audio signal by mapping the mixed audio signal to an embedding space, to obtain an embedding feature of the mixed audio signal in the embedding space; and generalized feature extraction is performed on the embedding feature, so that a generalized feature of a target component in the mixed audio signal can be obtained through extraction. The generalized feature of the target component has good generalization capability and expression capability, and can be used for different scenarios. Audio signal processing is performed on the mixed audio signal based on the generalized feature of the target component to obtain information of the audio signal of the target object, thereby improving the robustness and generalization of an audio signal processing process, and improving the accuracy of audio signal processing.

Abstract: A speech synthesis method includes: converting a text input sequence into a text feature representation sequence; inputting the text feature representation sequence into an encoder including N encoding layers; the N encoding layers including an encoding layer Ei and an encoding layer Ei+1; the encoding layer Ei+1 including a first multi-head self-attention network; acquiring a first attention matrix and a historical text encoded sequence outputted by the encoding layer Ei, and generating a second attention matrix of the encoding layer Ei+1 according to residual connection between the first attention matrix and the first multi-head self-attention network and the historical text encoded sequence; and generating a target text encoded sequence of the encoding layer Ei+1 according to the second attention matrix and the historical text encoded sequence, and generating synthesized speech data matched with the text input sequence based on the target text encoded sequence.

Abstract: An abstractive technique and an extractive technique are used to generate concise natural-language summaries of related text documents. The abstractive step generates a machine summary by constructing a graph with nodes representing unique pairs of tokens and corresponding parts-of-speech (POS), and with edge sequences representing token/POS pairs comprising sentences of a corresponding topic group from the text documents. Ranked candidate summary sentences are generated using subgraphs of the graph having initial and final nodes corresponding with valid sentence start and end pairs. The machine summary includes representative summary sentence(s) selected from each topic group's ranked candidates. The extractive step generates a natural-language summary from the machine summary by computing, for each topic group, numerical suitability measures providing comparisons between the representative summary sentence and sentences of the topic group.

Abstract: A speech recognition system includes, or has access to, conventional speech recognizer data, including a conventional acoustic model and pronunciation dictionary. The speech recognition system generates restructured speech recognizer data from the conventional speech recognizer data. When used at runtime by a speech recognizer module, the restructured speech recognizer data produces more accurate and efficient results than those produced using the conventional speech recognizer data. The restructuring involves segmenting entries of the conventional pronunciation dictionary and acoustic model according to their constituent phonemes and grouping those entries with the same initial N phonemes, for some integer N (e.g., N=3), and deriving a restructured dictionary with a corresponding semi-word acoustic model for the various grouped entries.

Abstract: A method for training a speech recognition model includes obtaining a multilingual text-to-speech (TTS) model. The method also includes generating a native synthesized speech representation for an input text sequence in a first language that is conditioned on speaker characteristics of a native speaker of the first language. The method also includes generating a cross-lingual synthesized speech representation for the input text sequence in the first language that is conditioned on speaker characteristics of a native speaker of a different second language. The method also includes generating a first speech recognition result for the native synthesized speech representation and a second speech recognition result for the cross-lingual synthesized speech representation. The method also includes determining a consistent loss term based on the first speech recognition result and the second speech recognition result and updating parameters of the speech recognition model based on the consistent loss term.

Abstract: Systems and processes for providing differentiated advertising sponsorship of a fabricated reading product are provided. Natural language digital text characterized by a sentence, comprised of words is user selected. The text is linguistically analyzed in furtherance of displaying a fabricated reading product corresponding to the text. The words of the sentence of the text, and the sentences, are evaluated with regard to word/sentence attributes for the words of the sentences text in furtherance of supplying only the word/sentence attributes to an advertising sponsor. Based upon either or both of the word and sentence evaluation, determining whether to supply an ad from the advertising sponsor in relation to a display of the fabricated reading product, and, in connection to supplying an ad, further determining placement position of the ad in relation to the display of the fabricated reading product.

Abstract: Methods, apparatuses, and computer program products for multilingual simultaneous interpretation using a distributed ledger are disclosed. A multilingual interpretation server determines that a new word has been added to a first language node, the first language node corresponding to a first language, and broadcasts, to a plurality of other language nodes, a request to interpret the new word, wherein each of the plurality of other language nodes corresponds to a different language. Each of the plurality of language nodes interprets the new word into a particular language and adds the new word and one or more interpretations of the new word to an entry in a dictionary ledger. A multilingual interpretation service provides simultaneous multilingual translations of from a source language to a plurality of target languages using the shared distributed dictionary ledger. A multilingual interpretation client is provided for accessing services provided by the multilingual interpretation service.

Abstract: A method executed by a computing device includes determining a set of identigens for each query word of a query to produce sets of identigens, where a set of identigens represents different meanings of a word of the query. The method further includes obtaining a first identigen selection for a first query word from the first set of identigens. The method further includes interpreting, using identigen pairing rules and based on the first identigen selection, the sets of identigens to produce a query entigen group. The method further includes accessing a knowledge database utilizing the query entigen group to produce a response entigen group. The method further includes generating a response to the query using the response entigen group, where the response includes at least one response word.

Abstract: A method including detecting a webpage accessed by a user on a computing device via a browser. Content on the webpage is determined, and a model is applied to the content to determine a plurality of keyword sets. A network search is performed based on each of the plurality of keyword sets to generate a plurality of search results. The plurality of search results are compared to the content, and the plurality of search results are compared to each other. A factualness of the content is determined based on the comparing of the plurality of search results to the content and based on the comparing of the plurality of the search results to each other, and the user is notified via the browser of the factualness of the content.

Abstract: A method of providing sentiment analysis includes training a computer system to identify a polarity of a sentiment for a plurality of phrases and receiving, at the computer system, an input from a website. The input includes a text string including a phrase. The method includes determining, by the computer system, the polarity of the sentiment of the phrase by comparing the phrase to the plurality of phrases and sending, by the computer system, a command to the website that causes the website to display predetermined content based on the determined polarity of the sentiment of the phrase.

Abstract: Systems and methods for natural language processing are described. Embodiments of the inventive concept are configured to receive an input sequence and a plurality of candidate long forms for a short form contained in the input sequence, encode the input sequence to produce an input sequence representation, encode each of the plurality of candidate long forms to produce a plurality of candidate long form representations, wherein each of the candidate long form representations is based on a plurality of sample expressions and each of the sample expressions includes a candidate long form and contextual information, compute a plurality of similarity scores based on the candidate long form representations and the input sequence representation, and select a long form for the short form based on the plurality of similarity scores.

Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: Described are systems, methods, apparatuses, and computer program product embodiments for automatically processing intent-based spoken language for SLU. The disclosed solution uses a scale-free network structured conversational knowledge graph that stores nodes representative of actions, objects, and intent names and edges representative of relationships between the nodes. For all phrases (including a sentence) from the same intent, the system calculates a mean feature vector using a Universal Sentence Embedding (USE) model as a feature element. The system also employs a multi-step intent detection strategy. A graph query technique may be used to match all potential intent nodes from the trained knowledge graph. The system may compute a covariance matrix between the feature element of an input phrase and feature elements of all potential intents. The major component of the covariance matrix along with the maximum covariance may be used to determine the final intent.

Abstract: Systems may perform analyses of claims included in a patent document. The systems may generate one or more search strings from the patent document and provide the one or more search strings to a third-party searching authority. The third-party searching authority may return a collection of documents responsive to the one or more search strings. In particular situations, the systems may re-rank the documents of the collection to provide a patent centric ranking. The systems may also analyze the documents of the collection with respect to the elements of the claims to generate various types of patent infringement and/or invalidity reports.

Abstract: A conference terminal and an embedding method of audio watermarks are provided. In the method, a first speech signal and a first audio watermark signal are received respectively. The first speech signal relates to a speaker corresponding to another conference terminal, and the first audio watermark signal corresponds to the another conference terminal. The first speech signal is assigned to a host path to output a second speech signal. The first audio watermark signal is assigned to an offload path to output a second audio watermark signal. The host path provides more digital signal processing (DSP) effects than the offload path. The second speech signal and the second audio watermark signal are synthesized to output a synthesized audio signal. The synthesized audio signal is adapted for audio playback. A completed audio watermark signal is outputted accordingly.