Amazon Technologies, Inc.

Pu Paul Zhao

Sravan Babu Bodapati

Katrin Kirchhoff

Varun Sembium Varadarajan

Stefano Stefani

Rahul Gupta

Mohit Gupta

Vikram Sathyanarayana Anbazhagan

Rishita Rajal Anubhai

Ashish Singh

Pramod Gurunath

Vasanth Philomin

Asem Rustum

Yue Yang

Vivek Govindan

Deepikaa Suresh

Vijit Gupta

Julian E S Salazar

Deepthi Devaiah Devanira

Christian Egon Berkhoff Dossow

Himalay Mohanlal Joriwal

Sai Madhuri Bhavirisetty

Abhinav Kumar

Ruoyu Huang

Orestis Lykouropoulos

Akshay Nalwaya

Liangwei Guo

Katrin Kirchoff

Yibin Wang

K P N V D S Siva Rama

Calvin Xuan Li

Techniques for managing access to sensitive data in transcriptions are described. A method for managing access to sensitive data in transcriptions may include receiving a request to generate a redacted transcript of content, obtaining a transcript of the content, sending at least a portion of the transcript to a model endpoint to identify sensitive entities in the transcript, receiving an inference response identifying one or more sensitive entities in the transcript, and generating the redacted transcript based at least one the transcript and the inference response.

Managing access to sensitive data in transcriptions

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

Techniques for extending sensitive data tagging without reannotating training data are described. A method for extending sensitive data tagging without reannotating training data may include hosting a plurality of models at a model endpoint in a machine learning service, each model trained to identify a different sensitive data type in a transcript of content, adding a new model to the model endpoint, the new model trained to identify a new sensitive data entity in the transcript of content, identifying sensitive entities in the transcript by each of the plurality of models and the new model, merging inference responses generated by each of the plurality of models and the new model using at least one inference policy, and returning a merged inference response identifying a plurality of sensitive entities in the transcript.

Extending sensitive data tagging without reannotating training data

Techniques for streaming real-time automated speech recognition (ASR) are described. A user can stream audio data to a frontend service of the ASR service. The frontend service can establish a bi-directional connection to an audio decoder host to perform ASR on the data stream. The audio decoder host may include a streaming ASR engine which can analyze chunks of the audio data stream using an acoustic model to divide the audio data into words, and a language model to identify sentences made of the words spoken in the audio file. The acoustic model can be trained using short audio sentence data (e.g., on the order of 30 seconds to a few minutes), enabling the transcription service to accurately transcribe short chunks of audio data. The results are then punctuated and normalized. The resulting transcript is then streamed back to the user over the bi-directional connection.

Streaming real-time automatic speech recognition service

Growing community of inventors

Seattle, WA, United States of America

Pu Paul Zhao