A method and a system for identifying users in a multi-user environment are disclosed. The method includes receiving audio data corresponding to an utterance of a voice command captured by a user device, the user device having a plurality of different users; for each of a plurality of different users of the user device, obtaining corresponding speaker verification data; generating a correspondingspeaker verification score using the corresponding speaker verification data and the audio data, the corresponding speaker verification score indicating the possibility that the utterance of the voicecommand is spoken by a corresponding one of the plurality of different users of the user device; identifying the speaker of the utterance of the voice command as a user of the plurality of differentusers of the user device associated with the highest corresponding speaker verification score; and using a voice recognition module to process the voice command to identify a particular action for theuser device to perform, when the particular action is executed by the user device, initiating an application on the user device to access the application based on the user permission associated withthe identified speaker.

Speaker verification using co-location information

System and method for speech recognition incorporating environmental variables are provided. The system comprises: a speech capture device (202); a feature extraction module (204); an environment variable module (206), wherein the environment variable module determines a value for an environment variable; and a speech recognition decoder (208), wherein the speech recognition decoder utilizes a deep neural network (DNN) to recognize speech captured by the speech capture device, wherein one or more components of the DNN are modeled as a set of functions of the environment variable.

/pdf/variable-component-deep-neural-network-for-robust-speech-1cswd18b0g.pdf

Variable-component deep neural network for robust speech recognition

The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.

End-to-end speaker recognition using deep neural network

Systems and methods are provided for generating a DNN classifier by "learning" a "student" DNN model from a larger, more accurate "teacher" DNN model. The student DNN may be trained from unlabeled training data by passing the unlabeled training data through the teacher DNN, which may be trained from labeled data. In one embodiment, an iterative processis applied to train the student DNN by minimizing the divergence of the output distributions from the teacher and student DNN models. For each iteration until convergence, the difference in the outputs of these two DNNsis used to update the student DNN model, and outputs are determined again, using the unlabeled training data. The resulting trained student DNN model may be suitable for providing accurate signal processing applications on devices having limited computational or storage resources such as mobile or wearable devices. In an embodiment, the teacher DNN model comprises an ensemble of DNN models.

Learning Student DNN Via Output Distribution

The invention discloses a session emotion autoanalysis method based on depth learning and belongs to the natural language processing and data mining field According to the method, voice and text expression is learned on the basis of a de-noising auto-encoder, further through a depth learning method, depth fusion of two types of expressions is realized to acquire unified high level expression, and emotion analysis is carried out on the basis of high level expression after fusion Through the method, depth fusion of acoustic and text characteristics is realized, and emotion classification accuracy is improved

Session emotion autoanalysis method based on depth learning

A method and device for voiceprint recognition, include: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a respective high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the respective high-level voiceprint feature sequence registered for the user.

Method and device for voiceprint recognition

The embodiment of the invention provides a voiceprint recognition method and device. The method comprises the steps that a deep neural network primary model is built on the basis of label-free voice data, the deep neural network primary model is trained on the basis of label voice data, and therefore a deep neural network secondary model is obtained; the speaker high-level voiceprint characteristic and speaker identification are registered by means of the deep neural network secondary model; test voice data are received, the test voice high-level voiceprint characteristic is extracted from the test voice data on the basis of the deep neural network secondary model, and the speaker identification is determined on the basis of the test voice high-level voiceprint characteristic. Due to the fact that deep neural network automatic learn voice signals comprise high-level speaker information capable of resisting noise interference, the noise interference can be effectively resisted.

Voiceprint recognition method and device

A computer-implemented method is performed at a server having one or more processors and memory storing programs executed by the one or more processors for authenticating a user from video and audio data. The method includes: receiving a login request from a mobile device, the login request including video data and audio data; extracting a group of facial features from the video data; extracting a group of audio features from the audio data and recognizing a sequence of words in the audio data; identifying a first user account whose respective facial features match the group of facial features and a second user account whose respective audio features match the group of audio features. If the first user account is the same as the second user account, retrieve the sequence of words associated with the user account and compare the sequences of words for authentication purpose.

User authentication method and apparatus based on audio and video data

The invention provides a user authentication method and device on basis of audios and videos. The method includes the steps of obtaining video image frames and voice frames provided when identities of users are registered; training face models and voiceprint models respectively by means of the video image frames and the voice frames and storing the face models, the voiceprint models and identifications of the users to a set storage space correspondingly; collecting video image frames and voice frames of any authenticated user during authentication, finding the face model matched with the collected video image frames from the set storage space, obtaining the first user identification corresponding to the found face model, finding the voiceprint model matched with the collected voice frames from the set storage space and obtaining the second user identification corresponding to the found voiceprint model; comparing whether the first user identification is the same as the second user identification and verifying whether the authenticated user passes through authentication according to the comparative result. By the adoption of the method and device, many risk issues caused by existing user authentication can be avoided.

User authentication method and device on basis of audios and videos

A method and a device for training an acoustic language model, include: conducting word segmentation for training samples in a training corpus using an initial language model containing no word class labels, to obtain initial word segmentation data containing no word class labels; performing word class replacement for the initial word segmentation data containing no word class labels, to obtain first word segmentation data containing word class labels; using the first word segmentation data containing word class labels to train a first language model containing word class labels; using the first language model containing word class labels to conduct word segmentation for the training samples in the training corpus, to obtain second word segmentation data containing word class labels; and in accordance with the second word segmentation data meeting one or more predetermined criteria, using the second word segmentation data containing word class labels to train the acoustic language model.

Method and device for acoustic language model training

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