Techniques are described for training and/or utilizing an end-to-end speaker diarization model. In various implementations, the model is a recurrent neural network (RNN) model, such as an RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer. Audio features of audio data can be applied as input to an end-to-end speaker diarization model trained according to implementations disclosed herein, and the model utilized to process the audio features to generate, as direct output over the model, speaker diarization results. Further, the end-to-end speaker diarization model can be a sequence-to-sequence model, where the sequence can have variable length. Accordingly, the model can be utilized to generate speaker diarization results for any of various length audio segments.

Described herein is a system for emotion detection in audio data using a speaker's baseline. The baseline may represent a user's speaking style in a neutral emotional state. The system is configured to compare the user's baseline with input audio representing speech from the user to determine a emotion of the user. The system may store multiple baselines for the user, each associated with a different context (e.g., environment, activity, etc.), and select one of the baselines to compare with the input audio based on the contextual situation.

Described herein is a system for emotion detection in audio data using a speaker's baseline. The baseline may represent a user's speaking style in a neutral emotional state. The system is configured to compare the user's baseline with input audio representing speech from the user to determine a emotion of the user. The system may store multiple baselines for the user, each associated with a different context (e.g., environment, activity, etc.), and select one of the baselines to compare with the input audio based on the contextual situation.

A method for controlling secure access to user requested data includes retrieving information related to potential unauthorized access to user requested data. The information is collected by a plurality of sensors of user's mobile device. A trained statistical model representing an environment surrounding a user is generated based on the retrieved information. A first data security value is determined using the generated trained statistical model. The first data security value indicates a degree of information security based on user's environment. A second data security value is determined using the generated trained statistical model. The second data security value indicates a degree of confidentiality of the user requested data. The user requested data is filtered based on a ratio of the determined first data security value and the second data security value.

A method for controlling secure access to user requested data includes retrieving information related to potential unauthorized access to user requested data. The information is collected by a plurality of sensors of user's mobile device. A trained statistical model representing an environment surrounding a user is generated based on the retrieved information. A first data security value is determined using the generated trained statistical model. The first data security value indicates a degree of information security based on user's environment. A second data security value is determined using the generated trained statistical model. The second data security value indicates a degree of confidentiality of the user requested data. The user requested data is filtered based on a ratio of the determined first data security value and the second data security value.

Attributes of electronic content from a meeting are identified and evaluated to determine whether sub-portions of the electronic content should or should not be attributed to a user profile. Upon determining that the sub-portion should be attributed to a user profile, attributes of the sub-portion of electronic content are compared to attributes of stored user profiles. A probability that the sub-portion corresponds to at least one stored user profile is calculated. Based on the calculated probability, the sub-portion is attributed to a stored user profile or a guest user profile.

Attributes of electronic content from a meeting are identified and evaluated to determine whether sub-portions of the electronic content should or should not be attributed to a user profile. Upon determining that the sub-portion should be attributed to a user profile, attributes of the sub-portion of electronic content are compared to attributes of stored user profiles. A probability that the sub-portion corresponds to at least one stored user profile is calculated. Based on the calculated probability, the sub-portion is attributed to a stored user profile or a guest user profile.

Disclosed is a method and apparatus with recognition for speaker recognition. The method includes determining whether an input feature vector corresponding to a voice signal of a speaker meets a candidate similarity criterion with at least one registered data included in a registration database, selectively, based on a result of the determining of whether the input feature vector meets the candidate similarity criterion, constructing a candidate list based on the input feature vector, determining whether a candidate input feature vector, among one or more candidate input feature vectors constructed in the candidate list in the selective constructing of the candidate list, meets a registration update similarity criterion with the at least one registered data, and selectively, based on a result of the determination of whether the candidate input feature vector meets the registration update similarity criterion, updating the registration database based on the candidate input feature vector.

Disclosed is a method and apparatus with recognition for speaker recognition. The method includes determining whether an input feature vector corresponding to a voice signal of a speaker meets a candidate similarity criterion with at least one registered data included in a registration database, selectively, based on a result of the determining of whether the input feature vector meets the candidate similarity criterion, constructing a candidate list based on the input feature vector, determining whether a candidate input feature vector, among one or more candidate input feature vectors constructed in the candidate list in the selective constructing of the candidate list, meets a registration update similarity criterion with the at least one registered data, and selectively, based on a result of the determination of whether the candidate input feature vector meets the registration update similarity criterion, updating the registration database based on the candidate input feature vector.

A shared mobility vehicle hosts a moving “info kiosk” that provides information assistance to potential passengers (or other individuals) and to on-board passenger. The approach is applicable to human-operated vehicles, and a particularly applicable to autonomous vehicles where no human operator is available to provide assistance.