Abstract: A system and method for detecting and resolving mis-transcriptions in a transcript generated by an automatic speech recognition system when transcribing spoken words. The system and method receive a machine language generated transcript of a speech signal by at least one of a first machine learning system and a second machine learning system, and analyze the machine language generated transcript to find a region of low confidence indicative of a mis-transcription and predict an improvement to the region of low confidence indicative of the mis-transcription. The system and method select a replacement word for the mis-transcription based on the predicted improvement to the region of low confidence and replace the mis-transcription by the replacement word to generate a corrected transcript.

Abstract: Generally discussed herein are devices, systems, and methods for generating an automatic interactive digital notebook completion model. A method can include receiving notebook content of an interactive digital notebook, the notebook content including a markdown cell followed by a code cell. The method can include generating input/output examples by, for each input/output example by masking one of (i) content of the markdown cell or (ii) content of the code cell resulting in a masked cell, identifying the masked cell and content of another cell of the markdown cell or the code that is not masked as an input for an input/output example, and identifying the content of the masked cell as an output for the input/output example. The method can include training, based on the input/output examples, a natural language processing model that generates a prediction of the content of a second masked cell as an output.

Abstract: The present application provides a summary generation model training method, apparatus, electronic device, and non-transitory computer readable storage medium. The summary generation model training method includes: obtaining a first vector set, where vectors in the first vector set are original encoding vectors which have been trained; generating a second vector set based on the first vector set, where the number of vectors in the second vector set is greater than the number of the vectors in the first vector set, and each vector in the second vector set is determined according to one or more vectors in the first vector set; and taking the vectors included in the first vector set and the vectors included in the second vector set as input encoding vectors to perform model training to obtain a summary generation model.

Abstract: A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.

Abstract: A speech emotion detection system may obtain to-be-detected speech data. The system may generate speech frames based on framing processing and the to-be-detected speech data. The system may extract speech features corresponding to the speech frames to form a speech feature matrix corresponding to the to-be-detected speech data. The system may input the speech feature matrix to an emotion state probability detection model. The system may generate, based on the speech feature matrix and the emotion state probability detection model, an emotion state probability matrix corresponding to the to-be-detected speech data. The system may input the emotion state probability matrix and the speech feature matrix to an emotion state transition model. The system may generate an emotion state sequence based on the emotional state probability matrix, the speech feature matrix, and the emotional state transition model. The system may determine an emotion state based on the emotion state sequence.

Abstract: The application belongs to the field of big data, and particularly relates to an official document processing method, device, computer equipment and storage medium. The method includes the following steps of: performing format analysis on the to-be-reviewed official document, then acquiring the to-be-reviewed official document of standard file type, and identifying all file components and contents in the to-be-reviewed official document of standard file type; performing text format detection, text content detection and frame layout detection synchronously by a preset text processing model, obtaining a format detection result, a content detection result and a layout detection result; generating a detected error content according to the format detection result, content detection result and layout detection result, calling out a standard writing rule corresponding to the detected error content, marking the detected error content and the standard writing rule in the to-be-reviewed official document.

Abstract: Techniques for presenting an alternative input representation to a user for testing and collecting processing data are described. A system may determine that a received spoken input triggers an alternative input representation for presenting. The system may output data corresponding to the alternative input representation in response to the received spoken input, and the system may receive user feedback from the user. The system may store the user feedback and processing data corresponding to processing of the alternative input representation, which may be later used to update an alternative input component configured to determine alternative input representations for spoken inputs.

Abstract: A system includes: a natural language processing (NLP) model trained in a training domain and configured to perform natural language processing on an input dataset; an accuracy module configured to: calculate a domain shift metric based on the input dataset; and calculate a predicted decrease in accuracy of the NLP model attributable to domain shift relative to the training domain based on the domain shift metric; and a retraining module configured to selectively trigger a retraining of the NLP model based on the predicted decrease in accuracy of the NLP model.

Abstract: Examples of the present disclosure describe systems and methods for dynamically expanding acronyms in audio content. In aspects, a user access of an audio resource may be detected. The audio content of the audio resource may be evaluated to identify acronyms. One or more of the identified acronyms may be evaluated based on a user-specific context of the user and/or a global context associated with the user. Based on the evaluated context(s), expansion candidates and corresponding confidence scores may be determined for each identified acronym. Based on the confidence scores, an expansion candidate may be selected and used to replace the identified acronym when the audio content is consumed by the user.

Abstract: An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.

Abstract: A method and system for predicting an intended time interval for a content segment may include receiving a request for natural language processing (NLP) of the content segment, the content segment including one or more temporal expressions, accessing contextual data associated with each of the one or more temporal expressions, decoding the content segment into a program that describes a temporal logic of the content segment based on the one or more temporal expressions, evaluating the program using the contextual data to predict an intended time interval for the content segment, and providing the intended time interval as an output.

Abstract: Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement machine learning configured with a meta-learning training set (small dataset), to create a common-sense predictive language model, thus boosting the performance for downstream tasks. An embodiment operates by receiving a base sentence and perturbation sentences as an input and tokenizing the input to generate a sequence of tokens. Tokens of the semantic perturbation sentences are embedded with tokens of the base sentence as contextually similar tokens pairs to generate training data and classified to capture relationships of the base sentence and the perturbation sentences to generate a classification, which is used to train a language model.

Abstract: A voice call translation capability negotiation method and an electronic device are provided, and relate to the field of terminal technologies. The method includes: After a first electronic device establishes a communication link with a second electronic device, if a voice call translation function is enabled, the first electronic device receives first indication information sent by the second electronic device. The first indication information is used to indicate that a voice call translation function of the second electronic device is enabled. The first electronic device automatically disables the voice call translation function.

Abstract: A customizable communication system and method of use are described for providing dialect and language options for users to employ during interactions between the user and a third-party application, thereby enhancing user experience. In some embodiments, the system allows a user to select a plurality of dialect and language preferences while interacting with a third-party application offering voice command technology. The selected dialect and language preference is used during the interaction between the user and the third-party application.

Abstract: In cases in which a confidence score of an inferred intent label is a predetermined threshold or less, an intent inference section searches for whether or not wording pertaining to a location, such as “on the door”, is present in a question. In cases in which a word relating to a location is present, the intent inference section consults individual function identification data associated with OM item codes in order to find intent labels including individual functions relevant to the location (such as “door”). In cases in which an intent label including an individual function relevant to the “door” is found, an OMA interaction control section consults QA data to find and acquire associated response information based on the found intent label and the OM item code, and notifies a HMI interaction control section of such response information.

Abstract: Described is a system and method that determines character sequences from speech, without determining the words of the speech, and processes the character sequences to determine sentiment data indicative of emotional state of a user that output the speech. The emotional state may then be presented or provided as an output to the user.

Abstract: Participants may use one or more devices for engaging in a meeting, such as phones, conferencing devices, and/or computers. The devices include microphones that capture speech for determining the presence of distinct participants. Speech signals originating from different participants, or microphones, may be determined and associated with the participants. For example, microphones may be directional and more sensitive to sound coming from one or more specific directions than sound coming from other directions. By associating an individual with a microphone, or set of microphones, overlapping voices may be disambiguated to provide clear voice streams that aid in producing a clear transcript indicating the speech of the participants, respectively. An identity of the participants may be determined using voiceprint and/or voice recognition techniques.

Abstract: Systems and methods for distributing cloud-based language processing services to partially execute in a local device to reduce latency perceived by the user. For example, a local device may receive a request via audio input, that requires a cloud-based service to process the request and generate a response. A partial response may be generated locally and played back while a more complete response is generated remotely.

Abstract: A computer-implemented method for transcribing an utterance includes receiving, at a computing system, speech data that characterizes an utterance of a user. A first set of candidate transcriptions of the utterance can be generated using a static class-based language model that includes a plurality of classes that are each populated with class-based terms selected independently of the utterance or the user. The computing system can then determine whether the first set of candidate transcriptions includes class-based terms. Based on whether the first set of candidate transcriptions includes class-based terms, the computing system can determine whether to generate a dynamic class-based language model that includes at least one class that is populated with class-based terms selected based on a context associated with at least one of the utterance and the user.

Abstract: Techniques are described herein for enabling the use of “dynamic” or “context-specific” hot words to invoke an automated assistant. In various implementations, an automated assistant may be executed in a default listening state at least in part on a user's computing device(s). While in the default listening state, audio data captured by microphone(s) may be monitored for default hot words. Detection of the default hot word(s) transitions of the automated assistant into a speech recognition state. Sensor signal(s) generated by hardware sensor(s) integral with the computing device(s) may be detected and analyzed to determine an attribute of the user. Based on the analysis, the automated assistant may transition into an enhanced listening state in which the audio data may be monitored for enhanced hot word(s). Detection of enhanced hot word(s) triggers the automated assistant to perform a responsive action without requiring detection of default hot word(s).