activity
20172023
most citedJust ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

55 citations · 126 across the 10 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL2021

Low-Resource Adaptation of Open-Domain Generative Chatbots

Greyson Gerhard-Young, Raviteja Anantha, Srinivas Chappidi +1

Recent work building open-domain chatbots has demonstrated that increasing model size improves performance. On the other hand, latency and connectivity considerations dictate the m…

cs.CL2019★ 3 cited

End-to-end Anchored Speech Recognition

Yiming Wang, Xing Fan, I-Fan Chen +3

Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering bac…

cs.CL2018★ 1 cited

Scalable language model adaptation for spoken dialogue systems

Ankur Gandhe, Ariya Rastrow, Bjorn Hoffmeister

Language models (LM) for interactive speech recognition systems are trained on large amounts of data and the model parameters are optimized on past user data. New application inten…

cs.CL2018

LSTM-based Whisper Detection

Zeynab Raeesy, Kellen Gillespie, Zhenpei Yang +6

This article presents a whisper speech detector in the far-field domain. The proposed system consists of a long-short term memory (LSTM) neural network trained on log-filterbank en…

cs.CL2018★ 1 cited

Device-directed Utterance Detection

Sri Harish Mallidi, Roland Maas, Kyle Goehner +3

In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants. Applications include r…

cs.CL2017★ 55 cited

Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

Anjishnu Kumar, Arpit Gupta, Julian Chan +9

This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development…