Amazon Technologies, Inc.

Varun Sembium Varadarajan

Sravan Babu Bodapati

Katrin Kirchhoff

Pu Paul Zhao

Parminder Bhatia

Arun Kumar Ravi

Stefano Stefani

Rahul Gupta

Mohit Gupta

Vikram Sathyanarayana Anbazhagan

Ashish Singh

Monica Lakshmi Sunkara

Pramod Gurunath

Vasanth Philomin

Eric Chen

Ashish Vishwanath Shenoy

Asem Rustum

Yue Yang

Mohammed Khalilia

Thiruvarul Selvan Senthivel

Arjun Mukhopadhyay

Deepikaa Suresh

Emine Busra Celikkaya

Vivek Govindan

Julian E S Salazar

Deepthi Devaiah Devanira

Vijit Gupta

Fan Xiang

Orestis Lykouropoulos

Akshay Nalwaya

Katrin Kirchoff

Liangwei Guo

Yibin Wang

K P N V D S Siva Rama

Calvin Xuan Li

Vaibhav Lella

Ryan Hood

Borui Zhang

Sneha Avuthu

Tiberiu Mircea Doman

Ruoyu Huang

Christian Egon Berkhoff Dossow

Himalay Mohanlal Joriwal

Sai Madhuri Bhavirisetty

Abhinav Kumar

Language models may be dynamically updated for trending entities based on tuning data for particular users. A user may provide specific tuning data associated with trending entities within a class to generate a weight map for a language model. A class based model may be trained using the weight map specific for the user for the trending entities. Additionally, weights may be further boosted using a boosting language model to emphasize the trending entities.

Dynamic language model updates with boosting

Data sets may be enhanced to create data stores. Request to create data stores may be received. As part of performing the request to create a data store, items stored in an extensible data format may be identified for machine learning enhancement. Machine learning models may be applied to generate additional data from data in the items. The additional data may be added to extend the items and store the extended items in a new data store.

Enhancing data sets to create data stores

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 entity and relationship detect from unstructured text as a service are described. A service may receive a request to identify entities within a provided unstructured text element, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities. The service may send additional requests to an additional service or services implementing additional deep machine learning models to identify relationships between detected attributes and ones of the detected entities. The outputs from all services can be analyzed and consolidated into a single result that identifies the entities, any attributes of the entities, and confidence scores indicating the confidence in each detected entity.

Service architecture for entity and relationship detection in unstructured text

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

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Bothell, WA, United States of America

Varun Sembium Varadarajan