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» Classifying Recognition Results for Spoken Dialog Systems
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EMNLP
2009
13 years 5 months ago
Re-Ranking Models Based-on Small Training Data for Spoken Language Understanding
The design of practical language applications by means of statistical approaches requires annotated data, which is one of the most critical constraint. This is particularly true f...
Marco Dinarelli, Alessandro Moschitti, Giuseppe Ri...
ICASSP
2011
IEEE
12 years 11 months ago
Online detection of vocal Listener Responses with maximum latency constraints
When human listeners utter Listener Responses (e.g. back-channels or acknowledgments) such as ‘yeah’ and ‘mmhmm’, interlocutors commonly continue to speak or resume their ...
Daniel Neiberg, Khiet P. Truong
ACL
2004
13 years 9 months ago
Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems
We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-be...
Malte Gabsdil, Oliver Lemon
TREC
2000
13 years 9 months ago
The Thisl SDR System at TREC-9
This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large...
Steve Renals, Dave Abberley
NCI
2004
132views Neural Networks» more  NCI 2004»
13 years 9 months ago
A comparison between spiking and differentiable recurrent neural networks on spoken digit recognition
In this paper we demonstrate that Long Short-Term Memory (LSTM) is a differentiable recurrent neural net (RNN) capable of robustly categorizing timewarped speech data. We measure ...
Alex Graves, Nicole Beringer, Jürgen Schmidhu...