Abstract: A method for evaluating a verification model includes receiving a first and a second set of verification results where each verification result indicates whether a primary model or an alternative model verifies an identity of a user as a registered user. The method further includes identifying each verification result in the first and second sets that includes a performance metric. The method also includes determining a first score of the primary model based on a number of the verification results identified in the first set that includes the performance metric and determining a second score of the alternative model based on a number of the verification results identified in the second set that includes the performance metric. The method further includes determining whether a verification capability of the alternative model is better than a verification capability of the primary model based on the first score and the second score.

Abstract: A method, computer program product, and computing system for receiving feature-based voice data associated with a first acoustic domain. One or more reverberation-based augmentations may be performed on at least a portion of the feature-based voice data, thus defining reverberation-augmented feature-based voice data.

Abstract: A speech-processing system receives input data representing text. An encoder processes segments of the text to determine embedding data, and a decoder processes the embedding data to determine one or more categories associated with each segment. Output data is determined by selecting words based on the segments and categories.

Abstract: Implementations are described herein for recommending actions based on entity or entity type. In various implementations, a partial free-form natural language input may be received from a user at an input component of a computing device. The partial free-form natural language input may identify an entity without identifying a responsive action and may be directed by the user to an automated assistant that operates at least in part on the computing device. The partial free-form natural language input may be analyzed to identify the entity. Based on the identified entity, a plurality or superset of candidate responsive actions may be identified, filtered, and/or ranked based on one or more signals. The automated assistant may then provide output that recommends one or more of the candidate responsive actions based on the ranking and/or filtering.

Abstract: An audio emitter configured to emit a sound creates a high-frequency copy of the sound to be emitted. The high-frequency copy of the sound is superimposed over the sound, resulting in a composite signal. The composite signal is emitted by the emitter. The high-frequency copy is at a frequency inaudible to humans, enabling a receiver to identify the emitter and/or the sound.

Abstract: An adaptive echo cancellation system introduces an acoustic reference signal to audio content being transmitted to the speaker for playback. The acoustic reference signal is an out-of-band signal, such as an ultrasonic signal, which is typically not audible to humans. The microphone of the mobile device receives the audio content played back by the speaker as well as audio content introduced by the user (e.g., the speech of the user). The adaptive echo cancellation system detects the acoustic reference signal and determines a time delay between when the acoustic reference signal was introduced to the audio content and when the audio content including the acoustic reference signal was received by the mobile device. Echo is cancelled from the received audio content based on this determined time delay.

Abstract: A method of detecting live speech comprises: receiving a signal containing speech; obtaining a first component of the received signal in a first frequency band, wherein the first frequency band includes audio frequencies; and obtaining a second component of the received signal in a second frequency band higher than the first frequency band. Then, modulation of the first component of the received signal is detected; modulation of the second component of the received signal is detected; and the modulation of the first component of the received signal and the modulation of the second component of the received signal are compared. It may then be determined that the speech may not be live speech, if the modulation of the first component of the received signal differs from the modulation of the second component of the received signal.

Abstract: Audio signals representing a current utterance in a conversation and a dialog history including at least information associated with past utterances corresponding to the current utterance in the conversation can be received. The dialog history can be encoded into an embedding. A spoken language understanding neural network model can be trained to perform a spoken language understanding task based on input features including at least speech features associated with the received audio signals and the embedding. An encoder can also be trained to encode a given dialog history into an embedding. The spoken language understanding task can include predicting a dialog action of an utterance. The spoken language understanding task can include predicting a dialog intent or overall topic of the conversation.

Abstract: A method is disclosed for calculating similarity rates between electronic documents. The similarity rate is calculated based on a count of matching phrases between the electronic documents and distances between subsequent matching phrases in each of the electronic documents. A system is also disclosed for comparing the electronic documents to obtain their similarity rates. A computing device determines at least one first proximity parameter based on the number of matched words in a matching phrase and at least one second proximity parameter based on distances between the subsequent matching phrases in each of the electronic documents. The similarity rate is determined based on the first and second proximity parameters.

Abstract: An example apparatus for recognizing speech includes an audio receiver to receive a stream of audio. The apparatus also includes a key phrase detector to detect a key phrase in the stream of audio. The apparatus further includes a model adapter to dynamically adapt a model based on the detected key phrase. The apparatus also includes a query recognizer to detect a voice query following the key phrase in a stream of audio via the adapted model.

Abstract: Techniques for a digital assistant to receive intent input from a secondary user are provided. A digital assistant receives a query intent from a primary user, wherein the primary user is authorized to provide query intents. It is determined, based on the query intent, that intent input will be provided by a secondary user, wherein the secondary user is not authorized to provide query intents. Intent input provided by the secondary user is received. The digital assistant processes the query intent using the intent input. The results of the query intent are provided to the primary user.

Abstract: An approach for generating an optimized video of a speaker, translated from a source language into a target language with the speaker's lips synchronized to the translated speech, while balancing optimization of the translation into a target language. A source video may be fed into a neural machine translation model. The model may synthesize a plurality of potential translations. The translations may be received by a generative adversarial network which generates video for each translation and classifies the translations as in-sync or out of sync. A lip-syncing score may be for each of the generated videos that are classified as in-sync.

Abstract: Systems and methods for conducting communications between a user and an Artificial Intelligence (AI) character model are provided. An example method includes determining a context of a dialog between the AI character model and the user, the context being determined based on a data stream received from a client-side computing device associated with the user; receiving a message of the user in the dialog; and generating, based on the context and the message, an input to a language model configured to predict a response to the message; providing the input to the language model to obtain the response; and transmitting the response to the client-side computing device, where the client-side computing device presents the response to the user. The input to the language model includes the message expanded by a keyword associated with the context. The context includes an intent of the user and an emotional state of the user.

Abstract: Systems and methods are presented herein for providing a user with a notification, or access to content, based on the user's factual discourse during a conversation with other users. A first user may provide a first statement. A second user may provide a second statement. An application determines the first and the second statement are associated with first and second user profiles, respectively. The application analyzes the elements of each respective statement and determines there is a conflict between the user statements. In response to determining there is a conflict between the respective statements, the application generates a respective search query to verify each respective statement. When the application determines there is an answer that resolves the conflict between the respective statements, the application generates a notification for the users that comprises the answer that resolves the conflict and may include access to content affirming the answer.

Abstract: An automatic speech recognition (ASR) system recognizes speech expressed in different languages. The ASR system includes a language-agnostic encoding component and prediction component. A language-specific joint analysis system generates first-language probabilities for symbols of a first language and second-language probabilities for symbols of a second language, based on outputs generated by the encoding component and the prediction component. The ASR system then modifies the probabilities produced by the joint analysis system by language-specific weighting information that, in turn, is produced by an attention system. This yields modified first-language probabilities and modified second-language probabilities. Finally, the ASR system predicts an updated instance of label information based on the modified first-language probabilities and the modified second-language probabilities. The ASR system can be successfully applied to recognize an utterance that combines words in two or more languages.

Abstract: Disclosed embodiments relate to extracting classification information from input text.

Abstract: A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.

Abstract: The present disclosure provides a summary generation model training method and apparatus, a device and a storage medium, and relates to the field of computer technologies, and in particular, to the field of artificial intelligence such as natural language processing and deep learning. The summary generation model training method includes: acquiring a document representation corresponding to a document sample; constructing, based on the document representation, a summary representation corresponding to the document representation, the summary representation including a positive summary representation and a negative summary representation; and constructing a total contrastive loss function based on the document representation, the positive summary representation and the negative summary representation, and training a summary generation model based on the total contrastive loss function. The present disclosure may improve accuracy of the summary generation model.

Abstract: Implementations described herein include detecting a stream of audio data that captures a spoken utterance of the user and that captures ambient noise occurring within a threshold time period of the spoken utterance being spoken by the user. Implementations further include processing a portion of the audio data that includes the ambient noise to determine ambient noise classification(s), processing a portion of the audio data that includes the spoken utterance to generate a transcription, processing both the transcription and the ambient noise classification(s) with a machine learning model to generate a user intent and parameter(s) for the user intent, and performing one or more automated assistant actions based on the user intent and using the parameter(s).

Abstract: An embodiment for creating a dialog based on anticipated questions for database driven conversations is provided. The embodiment may include receiving content from a database. The embodiment may also include identifying one or more schemas, one or more entities, and relational data in the content. The embodiment may further include identifying a semantic type and a number of distinct entries for each entity. The embodiment may also include presenting choices for one or more query targets and one or more filtering conditions to a user. The embodiment may further include prompting the user for one or more annotations and one or more clarifying questions for each chosen query target and filtering condition. The embodiment may also include generating a plurality of modular phrases and combining the plurality of modular phrases into one or more sentences and paraphrases of the one or more sentences.