Abstract: A system and a method for creation of notes for items of interest mentioned in audio content is provided. The system detects a playback of audio content on a media device. The audio content includes a talk by a host or a conversation between the host and one or more persons. The system analyzes a portion of the audio content using a machine learning model and determines one or more items of interest that are mentioned in the talk or the conversation, based on the analysis. The system extracts information associated with the determined one or more items of interest from a web-based data source and controls the media device to display a user interface that includes the extracted information.

Abstract: Implementations relate to determining multilingual content to render at an interface in response to a user submitted query. Those implementations further relate to determining a first language response and a second language response to a query that is submitted to an automated assistant. Some of those implementations relate to determining multilingual content that includes a response to the query in both the first and second languages. Other implementations relate to determining multilingual content that includes a query suggestion in the first language and a query suggestion in a second language. Some of those implementations relate to pre-fetching results for the query suggestions prior to rendering the multilingual content.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing dialogs. In one aspect, a method includes receiving a request associated with a task from a user device; submitting the request to each of a plurality of distinct data providers; receiving a plurality of suggested dialog responses from two or more of the data providers; scoring the one or more suggested dialog responses based on one or more scoring factors; determining a particular dialog response to provide to the user based on the scoring; and providing the determined dialog response to the user device.

Abstract: An apparatus comprises at least one processing device configured to receive a query to determine associations between named entities and aspect terms for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying associations of the words, and to determine, for a given aspect term, attention weights for a given subset of the words surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, predictions of association between the given aspect term and named entities recognized in the given subset of the words, and to provide a response to the query comprising at least one of the predicted associations.

Abstract: An apparatus for synthesizing speech according to an embodiment is a computing apparatus that includes one or more processors and a memory storing one or more programs executed by the one or more processors. The apparatus for synthesizing speech includes a pre-processing module that marks a preset classification symbol on each of unit texts input; and a speech synthesis module that receives each unit text marked with the classification symbol and synthesizes speech uttering the unit text based on the input unit text.

Abstract: Computer generated speech can be generated for cross-lingual natural language textual data streams by utilizing a universal phoneme set. In a variety of implementations, the natural language textual data stream includes a primary language portion in a primary language and a secondary language portion that is not in the primary language. Phonemes corresponding to the secondary language portion can be determined from a set of phonemes in a universal data set. These phonemes can be mapped back to a set of phonemes for the primary language. Audio data can be generated for these phonemes to pronounce the secondary language portion of the natural language textual data stream utilizing phonemes associated with the primary language.

Abstract: Methods and systems are disclosed herein for improvements relating to compressed automatic speech recognition (ASR) systems. The ASR system may comprise a compressed acoustic engine and an adaptive decoder. The adaptive decoder may be dynamically compiled based on characteristics of the compressed acoustic engine and a current state of the application device. In some embodiments, a dynamic command list is used to manage context-specific commands. Two or more commands recognized by the adaptive decoder may be confusable due to compression of the ASR system. Alternate commands may be determined that are semantically equivalent but phonetically different than the confusable commands to reduce classification error of the adaptive decoder. An alternate command may replace one or more of the confusable commands in the adaptive decoder. In some embodiments, a user interface is displayed to a user of the ASR system to select the alternate command for replacement in the decoder.

Abstract: Systems, methods, and computer-readable media for automatically altering information within an electronic document based on an externally detected occurrence are disclosed. The systems and methods may involve accessing an electronic word processing document; displaying an interface presenting at least one tool for enabling an author of the electronic word processing document to define an electronic rule triggered by an external network-based occurrence; receiving, in association with the electronic rule, a conditional instruction to edit the electronic word processing document in response to the network-based occurrence; detecting the external network-based occurrence; and in response to the detection of the external network-based occurrence, implementing the conditional instruction and thereby automatically edit the electronic word processing document.

Abstract: A cognitive agent system provides a centralized capability for users to configure and deploy cognitive agents across multiple heterogeneous vendor platforms. The cognitive agent system provides a design environment that allows users to define skills, as well as a new conversation construct that supports more complex interactions with users. The cognitive agent system also includes a deployment environment that allows users to register users and cognitive agents, deploy skills and conversations, and monitor the activity of cognitive agents across multiple vendor platforms. These users may use the cognitive agent system to define skills and conversations once and then deploy the skills and conversations to multiple service endpoints across different vendor platforms. In addition, the cognitive agent system allows user to directly manage cognitive agents that are not specific to any particular vendor.

Abstract: Systems and processes for intelligent device identification are provided. In one example process, audio input may be sampled with a microphone at each of two or more of the plurality of electronic devices. A first electronic device of the plurality of electronic devices for determining a task associated with sampled audio input may be identified. The process may determine the task based on the sampled audio input with the first electronic device and identify identifying a second electronic device of the plurality of electronic devices for performing the task. The task be performed with the second electronic device. The second electronic device is not the first electronic device in some examples.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing a received directional voice command by identifying a plurality of contextual factors associated with at least one user in a plurality of users using a natural language processing algorithm; dynamically identifying the at least one user in the plurality of users based on an analysis of the identified contextual factors associated with the received directional voice command; generating a plurality of scenarios within a mixed reality environment based on the analysis of the identified contextual factors associated with received directional voice command; identifying a location associated with at least one received directional voice command within a plurality of directional voice commands; and validating the location associated with the at least one received directional voice command using a corpus knowledge algorithm.

Abstract: A computer-implemented method increases the efficiency of matching records from two sources. The method includes identifying a first source and a second source wherein each of the sources include one or more records and each record includes one or more attributes. The method further includes determining, based on a corpus, the one or more attributes and generating, based on the attributes, a set of feature vectors which vectors represent the one or more attributes. The method includes comparing each record in the first source against each record in the second source. The method further includes generating, in response to the comparing, a link confidence. The method also includes linking, in response to the link confidence being above a linking threshold, the associated records. The method includes determining a first feature vector of the set of feature vectors used in the linking, and outputting a set of results.

Abstract: Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.

Abstract: An audio encoder for encoding audio input data has: a preprocessor for preprocessing the audio input data to obtain audio data to be coded; a coder processor for coding the audio data to be coded; and a controller for controlling the coder processor so that, depending on a first signal characteristic of a first frame of the audio data to be coded, a number of audio data items of the audio data to be coded by the coder processor for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for coding the reduced number of audio data items for the first frame is stronger enhanced compared to a second number of information units for the second frame.

Abstract: This disclosure describes systems and techniques receiving a request for information from a user and, in response, outputting the requested information along with unsolicited, interesting content that is related to, yet nonresponsive to, the requested information. In some instances, if the requested information is unknown, the techniques may output an indication that the information is unknown, followed by the additional, unsolicited, interesting content.

Abstract: Systems and methods are provided for enabling different modes of operation based on a detected audio trigger. The systems and methods may generate an audio signature for a detected first sound and compare the audio signature with a plurality of registered audio signatures. In response to determining that the audio signature matches a first registered audio signature, the systems and methods may enable a first operational mode for a device that enables a first plurality of commands In response to determining that the audio signature matches a second registered audio signature, the systems and methods may enable a second operational mode for a device that enables a second plurality of commands, where the second plurality of commands are different from the first plurality of commands.

Abstract: An apparatus for encoding a speech signal by determining a codebook vector of a speech coding algorithm is provided. The apparatus includes a matrix determiner for determining an autocorrelation matrix R, and a codebook vector determiner for determining the codebook vector depending on the autocorrelation matrix R. The matrix determiner is configured to determine the autocorrelation matrix R by determining vector coefficients of a vector r, wherein the autocorrelation matrix R includes a plurality of rows and a plurality of columns, wherein the vector r indicates one of the columns or one of the rows of the autocorrelation matrix R, wherein R(i, j)=r(|i?j|), wherein R(i, j) indicates the coefficients of the autocorrelation matrix R, wherein i is a first index indicating one of a plurality of rows of the autocorrelation matrix R, and wherein j is a second index indicating one of the plurality of columns of the autocorrelation matrix R.

Abstract: The present disclosure discloses a voice conversion method. The method includes: obtaining a to-be-converted voice, and extracting acoustic features of the to-be-converted voice; obtaining a source vector corresponding to the to-be-converted voice from a source vector pool, and selecting a target vector corresponding to the target voice from the target vector pool; obtaining acoustic features of the target voice output by the voice conversion model by using the acoustic features of the to-be-converted voice, the source vector corresponding to the to-be-converted voice, and the target vector corresponding to the target voice as an input of the voice conversion model; and obtaining the target voice by converting the acoustic features of the target voice using a vocoder. In addition, a voice conversion apparatus and a storage medium are also provided.

Abstract: In general, disclosed is a device that includes one or more processors, coupled to the memory, configured to perform an energy analysis with respect to one or more audio objects, in the ambisonics domain, in the first time segment. The one or more processors are also configured to perform a similarity measure between the one or more audio objects, in the ambisonics domain, in the first time segment, and the one or more audio objects, in the ambisonics domain, in the second time segment. In addition, the one or more processors are configured to perform a reorder of the one or more audio objects, in the ambisonics domain, in the first time segment with the one or more audio objects, in the ambisonics domain, in the second time segment, to generate one or more reordered audio objects in the first time segment.

Abstract: The acoustic feature extraction means 82 extracts an acoustic feature, using predetermined parameters, from an acoustic pattern obtained as a result of processing on an acoustic signal. The language vector calculation means 83 calculates a language vector from a given label that represents an attribute of a source of the acoustic signal and that is associated with the acoustic pattern. The similarity calculation means 84 calculates a similarity between the acoustic feature and the language vector. The parameter update means 85 learns parameters so that the similarity becomes larger, and updates the predetermined parameters to the parameters obtained by learning.