most citedEnd-to-end Anchored Speech Recognition

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

eess.AS2019

DiPCo -- Dinner Party Corpus

Maarten Van Segbroeck, Ahmed Zaid, Ksenia Kutsenko +7

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four A…

cs.CL20193 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…

eess.AS2019

Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning

Ladislav Mošner, Minhua Wu, Anirudh Raju +5

For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean an…

cs.CL2018

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

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…