Abstract: The information processing method in the present disclosure is performed as below. At least one speech segment is detected from speech input to a speech input unit. A first feature quantity is extracted from each speech segment detected, the first feature quantity identifying a speaker whose voice is contained in the speech segment. The first feature quantity extracted is compared with each of second feature quantities stored in storage and identifying the respective voices of registered speakers who are target speakers in speaker recognition. The comparison is performed for each of consecutive speech segments, and under a predetermined condition, among the second feature quantities stored in the storage, at least one second feature quantity whose similarity with the first feature quantity is less than or equal to a threshold is deleted, thereby removing the at least one registered speaker identified by the at least one second feature quantity.

Abstract: A facility for identifying multi-word expressions in a natural language sentence is described. The facility provides the sentence to each of multiple natural language processing modules including a first module, a second module, and a third module. Each natural language processing module uses a different approach to identify a multi-word expression and a type of the multi-word expression. Upon determining that the multiple identifiers of the multi-word expression differ, the facility determines the multi-word expression using a resolution process, which can involve a logical rule set or a machine learning model.

Abstract: Systems and processes for operating a digital assistant are provided. In an example process, low-latency operation of a digital assistant is provided. In this example, natural language processing, task flow processing, dialogue flow processing, speech synthesis, or any combination thereof can be at least partially performed while awaiting detection of a speech end-point condition. Upon detection of a speech end-point condition, results obtained from performing the operations can be presented to the user. In another example, robust operation of a digital assistant is provided. In this example, task flow processing by the digital assistant can include selecting a candidate task flow from a plurality of candidate task flows based on determined task flow scores. The task flow scores can be based on speech recognition confidence scores, intent confidence scores, flow parameter scores, or any combination thereof. The selected candidate task flow is executed and corresponding results presented to the user.

Abstract: The present disclosure relates to a method and a terminal for performing word segmentation on text information, and a storage medium. The method includes: acquiring the text information and configuration information, in which the configuration information includes at least two first word segmentation rules; converting the first word segmentation rules into second word segmentation rules according to a predetermined rule; in response to determining that an intersection exists between character strings of the text information matched by two of the second word segmentation rules, determining that two first word segmentation rules corresponding to the two of the second word segmentation rules associated with the intersection conflict; and processing the text information according to the configuration information, and outputting a result of the word segmentation on the text information.

Abstract: A system for identifying audio data includes a feature extraction module receiving unknown input audio data and dividing the unknown input audio data into a plurality of segments of unknown input audio data. A similarity module receives the plurality of segments of the unknown input audio data and receives known audio data from a known source, the known audio data being divided into a plurality of segments of known audio data. The similarity module performs comparisons between the segments of unknown input audio data and respective segments of known audio data and generates a respective plurality of similarity values representative of similarity between the segments of the comparisons, the comparisons being performed serially. The similarity module terminates the comparisons if the similarity values indicate insufficient similarity between the segments of the comparisons, prior to completing comparisons for all segments of the unknown input audio data.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers.

Abstract: An electronic apparatus and a controlling method thereof are provided. The electronic apparatus includes a communication interface comprising communication circuitry, a memory, and a processor. The processor is configured to control the electronic apparatus to: receive a user voice for controlling an external device connected to the electronic apparatus from a user terminal through the communication interface, perform user authentication by comparing feature information obtained from the user voice with feature information pre-stored in the memory, obtain a control command for controlling the external device by analyzing the user voice based on the user being authenticated, and control the communication interface to transmit the control command to the external device.

Abstract: Techniques for dialog data collection are disclosed. In an embodiment, a method comprises providing a first graphical user interface (10) configured to receive first user input data, providing a second graphical user interface (20) configured to receive second user input data, asynchronously transmitting the first user input data to the second graphical user interface (20) or the second user input data to the first graphical user interface (10), and generating training data for a natural language processing system model (60) based on the first user input data and the second user input data.

Abstract: A speech signal is received by a device comprising first and second transducers, and the first transducer comprises a microphone. A method comprises performing a first voice biometric process on speech contained in a first part of a signal received by the microphone, in order to determine whether the speech is the speech of an enrolled user. A first correlation is determined, between said first part of the signal received by the microphone and a corresponding part of the signal received by the second transducer. A second correlation is determined, between said second part of the signal received by the microphone and the corresponding part of the signal received by the second transducer. It is then determined whether the first correlation and the second correlation satisfy a predetermined condition.

Abstract: A method includes obtaining a string of words and determining whether two or more words of the string of words are in a word group. When the two or more words are in the word group, the method further includes retrieving a set of word group identigens for the word group and retrieving sets of word identigens for remaining words of the string of words. The method further includes determining whether a word group identigen of the set of word group identigens and word identigens of the sets of word identigens creates an entigen group that is a valid interpretation of the string of words. When the entigen group is the valid interpretation of the string of words, the method further includes outputting the entigen group.

Abstract: With recent real-world applications of speaker and speech recognition systems, robust features for degraded speech have become a necessity. In general, degraded speech results in poor performance of any speech-based system. This poor performance can be attributed to feature extraction functionality of speech-based system which takes input speech file and converts it into a representation called as a feature. Embodiments of the present disclosure provide systems and methods that compute distance between each degraded speech feature extracted from an input speech signal with each clean speech feature comprised in a memory of the system to obtain set of matched clean speech features wherein at least a subset of cleaned speech features are dynamically selected based on a pre-defined threshold and the computed distance, thereby computing statistics for the dynamically selected clean speech features set for utilizing in at least one of a speech recognition system and a speaker recognition system.

Abstract: Implementations relate to automatic generation of speaker features for each of one or more particular text-dependent speaker verifications (TD-SVs) for a user. Implementations can generate speaker features for a particular TD-SV using instances of audio data that each capture a corresponding spoken utterance of the user during normal non-enrollment interactions with an automated assistant via one or more respective assistant devices. For example, a portion of an instance of audio data can be used in response to: (a) determining that recognized term(s) for the spoken utterance captured by that the portion correspond to the particular TD-SV; and (b) determining that an authentication measure, for the user and for the spoken utterance, satisfies a threshold. Implementations additionally or alternatively relate to utilization of speaker features, for each of one or more particular TD-SVs for a user, in determining whether to authenticate a spoken utterance for the user.

Abstract: A mobile device, a system and a method for task management based on voice intercom function are provided. A mobile device receives a voice message associated with at least one task. Semantic information of the voice message is analyzed to determine at least one message receiver of the voice message and generate a task message. Another mobile device corresponding to one of the at least one message receiver receives the task message. Task management information associated with the at least one task is updated according to the semantic information of the voice message.

Abstract: A method and system of automatically interpreting documents relating to regulatory directives to automatically identify actionable items and assigning each of the actionable items identified to the appropriate responsible party in a business.

Abstract: A computing device that receives a real-time chat discourse. The computing device analyses the real-time chat discourse by consecutively applying a topic analysis technique, a corpus linguistics technique and a cosine similarity technique. The computing device derives a discourse decision forking component (DDFC) based on comparing the analyzed real-time chat discourse to a similarity threshold value and determines one or more discourse forks using the DDFC.

Abstract: A method and a voice processor that includes (i) an input that is configured to receive of audio signals that represent audio, (ii) a wake word detection circuit, (iii) a first buffer that is configured to store at least wake word signals and prebuffer signals, and (iv) a communication module that is configured to (a) output, over an interrupt port, an interrupt request to an application processor, following a detection of the wake word signals, (b) following an acceptance of the application processor to receive content, access the first buffer and retrieve the prebuffer signals and the wake word signals; and (b) output the content, over the I2S port, to the application processor. The content includes the wake word signals, the prebuffer signals, and query or command signals.

Abstract: Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Abstract: The present disclosure provides methods and systems for generating linguistic rules. The system may comprise: an electronic display with a graphical user interface comprising: (i) one or more interactive elements for receiving an user input indicating one or more edits to a rule, and (ii) a result visualization region for dynamically displaying a result of the rule in response to receiving the one or more edits, wherein the result of the rule comprises an indicator indicating the validity of the rule; and one or more computer processors that are programmed to: (i) generate the result of the rule by processing the rule with the one or more edits against a set of examples; and (ii) configure the graphical user interface to display the result in a user-selected format.

Abstract: For determining structure from a language block, a processor determines phrase tags from phrase vectors for phrases of a language block. The phrase tags specify a phrase function. The processor further determines structure tags for the phrases from the language block.