Amazon Technologies, Inc.

Julian E S Salazar

Katrin Kirchhoff

Yi Zhang

Rahul Gupta

Peng Zhang

Sravan Babu Bodapati

Mohit Gupta

Varun Sembium Varadarajan

Pu Paul Zhao

Asem Rustum

Arshit Gupta

Vivek Govindan

Liangwei Guo

Yibin Wang

K P N V D S Siva Rama

Calvin Xuan Li

Christian Egon Berkhoff Dossow

Himalay Mohanlal Joriwal

Sai Madhuri Bhavirisetty

Abhinav Kumar

Orestis Lykouropoulos

Akshay Nalwaya

A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.

Multi-modal spoken language understanding systems

A transcription service may receive a request from a developer to build a custom speech-to-text model for a specific domain of speech. The custom speech-to-text model for the specific domain may replace a general speech-to-text model or add to a set of one or more speech-to-text models available for transcribing speech. The transcription service may receive a training data and instructions representing tasks. The transcription service may determine respective schedules for executing the instructions based at least in part on dependencies between the tasks. The transcription service may execute the instructions according to the respective schedules to train a speech-to-text model for a specific domain using the training data set. The transcription service may deploy the trained speech-to-text model as part of a network-accessible service for an end user to convert audio in the specific domain into texts.

Model training system for custom speech-to-text models

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San Francisco, CA, United States of America

Julian E S Salazar