Amazon Technologies, Inc.

Itay Teller

Oguz Hasan Elibol

Andrew Jay Roths

Roberto Barra Chicote

Hongda Mao

Yixiong Meng

Andreas Stolcke

Udhgee Murugesan

Mansour Ahmadi

Eunjung Han

Zhenning Tan

Kevin Kwanghoon Ha

Ruirui Li

Roger Hau-Bin Cheng

Yuguang Yang

Kian Jamali Abianeh

Mohamed G Mahmoud

Techniques for performing voice-based user recognition are described. When a device receives audio data corresponding to a spoken user input, the device may generate spoken user input embedding data representing speech characteristics of the spoken user input. The device may also identify user embedding data representing speech characteristics of a user known to the device. A machine learning (ML) model may process the spoken user input embedding data to generate reduced spoken user input embedding data including a reduced number of dimensions, where the reduced number of dimensions are functions of the higher number of dimensions of the spoken user input embedding data. Moreover, the ML model may process the user embedding data to generate reduced user embedding data including a reduced number of dimensions, where the reduced number of dimensions are functions of the higher number of dimensions of the user embedding data and are tuned to characteristics of users of the device (as opposed to a representation of all possible users). A comparison of the reduced spoken user input embedding data and the reduced user embedding data may be used to determine whether the user spoke the user input.

Voice-based user recognition

Techniques for determining whether audio is machine-outputted or non-machine-outputted are described. A device may receive audio, may process the audio to determine audio data including audio features corresponding to the audio, and may process the audio data to determine audio embedding data. The device may process the audio embedding data to determine whether the audio is machine-outputted or non-machine-outputted. In response to determining that the audio is machine-outputted, then the audio may be discarded or not processed further. Alternatively, in response to determining that the audio is non-machine-outputted (e.g., live speech from a user), then the audio may be processed further (e.g., using ASR processing).

Detecting machine-outputted audio

Growing community of inventors

Sunnyvale, CA, United States of America

Itay Teller