Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Rui Zhao

Jinyu Li

Gerard Guy Medioni

Yifan Gong

Sarangarajan Parthasarathy

Manoj Aggarwal

Hosam Adel Khalil

Lei He

Jian Wu

Khuram Shahid

Emilian Y Stoimenov

Guoli Ye

Jian Xue

Kaustubh Prakash Kalgaonkar

Edward C Lin

Baomin Wang

Yao Tian

Umer Shahid

Georgios Skolianos

Wenning Wei

Eva Sharma

Ivaylo Andreanov Enchev

Anthony Phillip Stark

William Maxwell Gale

Mahadevan Srinivasan

Tianyi Wang

Yan Huang

Systems and methods are provided for accessing a factorized neural transducer comprising a first set of layers for predicting blank tokens and a second set of layers for predicting vocabulary tokens, the second set of layers comprising a language model that includes a vocabulary predictor which is a separate predictor from the blank predictor, wherein a vocabulary predictor output from the vocabulary predictor and the encoder output are used for predicting a vocabulary token. The second set of layers is selectively modified to facilitate an improvement in an accuracy of the factorized neural transducer in performing automatic speech recognition, the selectively modifying comprising applying a particular modification to the second set of layers while refraining from applying the particular modification to the first set of layers.

Fast and efficient text only adaptation for factorized neural transducer

An input device determines presence of an actual user, instead of an artifact, by using multi-wavelength reflectance spectroscopy. Light sources are operated to illuminate an object with different colors of light at different times. A detector determines, at those different times, intensity data indicative of intensity light of these different colors as reflected from the object. The intensity data is processed to determine whether the object is part of a user or is an artifact. For example, if the object is deemed to be a user, biometric input may be acquired. The biometric input may then be processed to identify the user. The input device may be used at various locations, such as at an entry portal, point of sale, and so forth.

System to determine user presence

Systems, methods, and devices are provided for generating and using text-to-speech (TTS) data for improved speech recognition models. A main model is trained with keyword independent baseline training data. In some instances, acoustic and language model sub-components of the main model are modified with new TTS training data. In some instances, the new TTS training is obtained from a multi-speaker neural TTS system for a keyword that is underrepresented in the baseline training data. In some instances, the new TTS training data is used for pronunciation learning and normalization of keyword dependent confidence scores in keyword spotting (KWS) applications. In some instances, the new TTS training data is used for rapid speaker adaptation in speech recognition models.

Generating and using text-to-speech data for speech recognition models

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Bellevue, WA, United States of America

Rui Zhao