Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a call. The system identifies one or more speakers in a call using one or more speaker-identification parameters. The system processes the call to determine one or more speaker-identification parameters, such as a transcript of the call, a facial image of the speaker, a scene image, which is an image of a scene in which the speaker is located during the call, or textual data associated with the call such as names of the speaker or an organization that are retrieved from the scene images or video data of the call. The system analyzes one or more of the speaker-identification parameters and determines the identity of the speaker. The system then identifies the voice segments associated with the identified speaker and marks the voice segments with the identity of the speaker.

Abstract: Methods and systems for causing a voice-activated electronic device to identify that a step of a series of steps can begin while a previous step is ongoing. In some embodiments, a first step will have a waiting period. The methods and systems, in some embodiments, identify this waiting period and determine that a second step can begin during the waiting period of step one. In some embodiments, nested sets of sequential steps are identified within the series of steps. The nested sets of sequential steps, in some embodiments, can be called upon.

Abstract: Audio data from a first, source speaker is received and processed to determine linguistic units and vocal characteristics corresponding to those linguistic units. The linguistic units may either be determined from received text data or may be determined from the audio data using automatic speech recognition. A model is trained using training data from a second, target speaker. The trained model concatenates the linguistic units with the vocal characteristics to produce output speech that has the “voice” of the target speaker and the vocal characteristics of the source speaker.

Abstract: A method and apparatus for controlling a device are disclosed. The method includes: performing voice recognition on a received sound signal to obtain a voice recognition result; determining keywords using the voice recognition result; determining a target intelligent device having attribute information matched with the keywords from intelligent devices, where relationships between the intelligent devices and attribute information of the intelligent devices are constructed in advance, and the attribute information characterizes a device operation provided by the intelligent device corresponding to the attribute information; and controlling the target intelligent device to perform an operation indicated by the voice recognition result.

Abstract: Disclosed are a speech recognition device and a speech recognition method which perform speech recognition by executing an artificial intelligence (AI) algorithms and/or a machine learning algorithm installed thereon, to communicate with other electronic devices and an external server in a 5G communication environment. The speech recognition method according to an embodiment of the present disclosure may include converting a series of spoken utterance signals to a text item, extracting a discordant named-entity that is discordant with a parent domain inferred form the text, calculating probabilities of candidate words associated with the discordant named-entity based on calculated distances between a term representing the parent domain and each candidate word associated with the discordant named-entity, and based on the calculated probabilities, modifying the discordant named-entity in the text to one of the candidate words associated with the discordant named-entity.

Abstract: Methods and systems are provided for discriminating ambiguous expressions to enhance user experience. For example, a natural language expression may be received by a speech recognition component. The natural language expression may include at least one of words, terms, and phrases of text. A dialog hypothesis set from the natural language expression may be created by using contextual information. In some cases, the dialog hypothesis set has at least two dialog hypotheses. A plurality of dialog responses may be generated for the dialog hypothesis set. The dialog hypothesis set may be ranked based on an analysis of the plurality of the dialog responses. An action may be performed based on ranking the dialog hypothesis set.

Abstract: A system is provided for handling errors during automatic speech recognition by processing a potentially defective utterance to determine an alternative, potentially successful utterance. The system processes an ASR hypothesis, using a probabilistic graph, to determine a likelihood that it will result in an error. Using the probabilistic graph, the system determines an alternate utterance.

Abstract: Disclosed is an intelligent presentation method. The intelligent presentation method of the present disclosure may support a presentation to be smoothly performed by learning content of the presentation while a presenter is presenting and performing a function required for the presentation in response to a command voice. The intelligent presentation-assisting device of the present disclosure may be associated with an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

Abstract: An agent device includes one or more agent controllers configured to provide a service including causing an output device to output a response of voice according to a voice of an occupant which is collected in a vehicle interior of a vehicle, a receiver configured to receive an input from the occupant, and a starting method setter configured to change or add a starting method of the agent controller on the basis of content received by the receiver.

Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: A corpus pattern paraphrasing method, system, and non-transitory computer readable medium, include aligning slots of patterns for verbal phrases based on syntactical and lexical features along with calculated synonyms to predict paraphrases that are not previously stored in a corpus of sentences in a database.

Abstract: A user adaptive conversation apparatus generating a talk for a conversation based on emotional and ethical states of a user. A voice recognition unit converts a talk of the user in a conversational situation into a natural language script form to generate talk information. An artificial visualization unit generates situation information by recognizing talking situation from a video and generates intention information indicating an intention of the talk. A natural language analysis unit converts the situation information and the intention information into the natural language script form. A natural language analysis unit analyzes the talk information, the intention information, and the situation information.

Abstract: A method and apparatus for generating a caption are provided. The method of generating a caption according to one embodiment comprises: generating caption text which corresponds to a voice of a speaker included in broadcast data; generating reference voice information using a part of the voice of the speaker included in the broadcast data; and generating caption style information for the caption text based on the voice of the speaker and the reference voice information.

Abstract: A method includes determining, by an electronic device, a skill from a first natural language (NL) input. Upon successful determination of the skill, the first NL input is transmitted to a custom skill parser for determination of a skill intent. The custom skill parser is trained based on data including at least a custom training data set. Upon unsuccessful determination of the skill, the first NL input is transmitted to a generic parser for determination of a general intent of the first NL input.

Abstract: A natural language processing apparatus includes: a first calculation unit configured to calculate a distributed vector of a word included in a plurality of sentences based on a database that manages the plurality of sentences associated with a classification word; a second calculation unit configured to calculate a distributed vector of the sentence based on the distributed vector of the word included in each sentence; and a third calculation unit configured to calculate a distributed vector of the classification word based on the distributed vector of each sentence associated with the same classification word.

Abstract: Techniques for improving adaptive interference cancellation (AIC) using cascaded AIC algorithms are described. To improve an accuracy of detecting speech, a device may perform a first stage of AIC to generate isolated audio data and may generate speech mask data indicating time windows when speech is detected in the isolated audio data. Based on the speech mask data, the device may perform second AIC to generate output audio data, with adaptation of the adaptive filter enabled when the speech is not detected and disabled when the speech is detected. Thus, the first AIC improves the accuracy with which the device detects that speech is present and the second AIC reduces distortion in the output audio data by not updating filter coefficient values when the speech is present. The first AIC may use playback audio data, microphone audio data or beamformed audio data as reference signals.

Abstract: A method includes determining, by an electronic device, a skill from a first natural language (NL) input. Upon successful determination of the skill, the first NL input is transmitted to a custom skill parser for determination of a skill intent. The custom skill parser is trained based on data including at least a custom training data set. Upon unsuccessful determination of the skill, the first NL input is transmitted to a generic parser for determination of a general intent of the first NL input.

Abstract: A method for waking up a robot includes: acquiring sight range information when a voice command issuer issues a voice command; if the sight range information of the voice command issuer when issuing the voice command is acquired, determining, based on the sight range information, whether the voice command issuer gazes the robot when the voice command is issued; and determining that the robot is called if the voice command issuer gazes the robot.

Abstract: An information processing device includes a processor. The processor is configured to: receive an input of a question; hold a response, when data required to output response content in response to the question is insufficient; and output, when insufficient data is collected while the response is being held, an announcement that the response is made and the response content.

Abstract: Systems, methods, and devices for outputting indications regarding voice-based interactions are described. A first speech-controlled device detects spoken audio corresponding to recipient information. The first device captures the audio and sends audio data corresponding to the captured audio to a server. The server determines a second speech-controlled device of the recipient and sends a signal to the recipient's second speech-controlled device representing a message is forthcoming. The recipient's second speech-controlled device outputs and indication representing a message is forthcoming.