Amazon Technologies, Inc.

Vasanth Philomin

Swaminathan Sivasubramanian

Vikram Sathyanarayana Anbazhagan

Ashish Singh

Stefano Stefani

Rama Krishna Sandeep Pokkunuri

Ganesh Kumar Gella

Yahor Pushkin

Roger Scott Jenke

Atul Deo

Harshal Pimpalkhute

Jessie Young

Anuroop Arora

Deepikaa Suresh

Yi Zhang

Vladimir Zhukov

Yaser Al-Onaizan

Swapandeep Singh

Pramod Gurunath

Varun Sembium Varadarajan

Meghana Puvvadi

Pu Paul Zhao

Manikya Pavan Kiran Pothukuchi

Saab Mansour

Santosh Kumar Ameti

Vijit Gupta

Salvatore Romeo

Katrin Kirchoff

Harsh Yadav

Praveen Kumar Akarapu

Rajkumar Gulabani

Priyanka Shirish Kale

Colin Thomas Davidson

Yasser El-Haggan

Ruoyu Huang

A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

Artificial agent generator

Using a first set of machine learning models, a communication from a user of a restaurant is analyzed at an order coordinator linked via a network to resources of an order management service at a provider network. A response to the communication is prepared using another set of models at the provider network and presented to the user. An order of the user for one or more restaurant menu items is fulfilled, based at least partly on analysis of a second communication received from the user after the response is presented.

Distributed system for automated restaurant order acquisition

Systems and methods to implement customer contact service with real-time supervisor assistance. A supervisor may oversee multiple agents in a customer contact service. A service of a computing resource service provider may monitor a plurality of audio connections at a service of a computing resource service provider, generate transcripts for the plurality of audio data, analyze the transcripts using a set of natural language processing (NLP) techniques to generate metadata, tag the transcripts with categories based at least in prat on the metadata, generate information for at least a portion of the plurality of connections based on the transcripts, metadata, and categories, and provide the information to a supervisor of the agents.

Customer contact service with real-time supervisor assistance

Systems and methods to analyze contacts data. Contacts data may be encoded as text (e.g., chat logs), audio (e.g., audio recordings), and various other modalities. A computing resource service provider may implement a service to obtain audio data from a client, transcribe the audio data, thereby generating text, execute one or more natural language processing techniques to generate metadata associated with the text, processing at least the metadata to generate an output, determine whether the output matches one or more categories, and provide the output to the client. Techniques described herein may be performed as an asynchronous workflow.

Systems and methods to analyze customer contacts

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

Techniques for automated speech recognition (ASR) are described. A user can upload an audio file to a storage location. The user then provides the ASR service with a reference to the audio file. An ASR engine analyzes the audio file, using an acoustic model to divide the audio data into words, and a language model to identify the words spoken in the audio file. The acoustic model can be trained using audio sentence data, enabling the transcription service to accurately transcribe lengthy audio data. The results are punctuated and normalized, and the resulting transcript is returned to the user.

Fully managed and continuously trained automatic speech recognition service

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Seattle, WA, United States of America

Vasanth Philomin