Microsoft Technology Licensing, LLC

Tencent America LLC

Shixiong Zhang

Xiong Xiao

Eyal Krupka

Xuedong D Huang

Wei Xiong

Adi Diamant

Amit Kumar Agarwal

Lijuan Qin

Karen Master Ben-Dor

Ido Leichter

Moshe David

Raz Halaly

Yoni Smolin

Aviv Hurvitz

Lingfeng Wu

Ilya Gurvich

Dong Yu

Jinyu Li

Yifan Gong

Yong Zhao

Zhuo Chen

A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Computerized intelligent assistant for conferences

Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives an audio signal of utterances spoken by multiple persons. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location and speaker identification neural network. The neural network utilizes both the magnitude and phase information features to determine a change in the person speaking. Output comprising the determination of the change is received from the neural network. The output is then used to perform a speaker recognition function, speaker location function, or both.

Speaker recognition/location using neural network

Examples are disclosed that relate to voice identification enrollment. One example provides a method of voice identification enrollment comprising, during a meeting in which two or more human speakers speak at different times, determining whether one or more conditions of a protocol for sampling meeting audio used to establish human speaker voiceprints are satisfied, and in response to determining that the one or more conditions are satisfied, selecting a sample of meeting audio according to the protocol, the sample representing an utterance made by one of the human speakers. The method further comprises establishing, based at least on the sample, a voiceprint of the human speaker.

Voice identification enrollment

This disclosure relates to a cloud-local joint or collaborative data analytics framework that provides data analytics models trained and hosted in backend servers for processing data items preprocessed and encrypted by remote terminal devices. The data analytics models are configured to generate encrypted output data items that are then communicated to the local terminal devices for decryption and post-processing. This framework functions without exposing decryption keys of the local terminal devices to the backend servers and the communication network. The encryption/decryption and data analytics in the backend servers are configured to process and communicate data items efficiently to provide real-time or near real-time system response to requests for data analytics from the remote terminal devices.

Distributed and collaborative analytics of encrypted data using deep polynomial networks

A speaker recognition system includes a previously-trained joint neural network. An enrollment machine of the speaker recognition system is configured to operate the previously-trained joint neural network to enroll a new speaker based on audiovisual data featuring the newly enrolled speaker. A recognition machine of the speaker recognition system is configured to operate the previously-trained joint neural network to recognize a previously-enrolled speaker based on audiovisual data featuring the previously-enrolled speaker.

Joint neural network for speaker recognition

Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives a multi-channel audio signal of an utterance spoken by a user. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location/speaker identification neural network that is trained via utterances from a plurality of persons. A user embedding comprising speaker identification characteristics and location characteristics is received from the neural network and compared to a plurality of enrollment embeddings extracted from the plurality of utterances that are each associated with an identity of a corresponding person. Based at least on the comparisons, the user is matched to an identity of one of the persons, and the identity of the person is outputted.

Improvements in speaker identification and verification are provided via an attention model for speaker recognition and the end-to-end training thereof. A speaker discriminative convolutional neural network (CNN) is used to directly extract frame-level speaker features that are weighted and combined to form an utterance-level speaker recognition vector via the attention model. The CNN and attention model are join-optimized via an end-to-end training algorithm that imitates the speaker recognition process and uses the most-similar utterances from imposters for each speaker.

Speaker recognition

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Redmond, WA, United States of America

Shixiong Zhang