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» Bi-Spectral Acoustic Features for Robust Speech Recognition
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ICMCS
2009
IEEE
189views Multimedia» more  ICMCS 2009»
13 years 5 months ago
Emotion recognition from speech VIA boosted Gaussian mixture models
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, ...
Hao Tang, Stephen M. Chu, Mark Hasegawa-Johnson, T...
ACIVS
2009
Springer
14 years 2 months ago
Two-Level Bimodal Association for Audio-Visual Speech Recognition
This paper proposes a new method for bimodal information fusion in audio-visual speech recognition, where cross-modal association is considered in two levels. First, the acoustic a...
Jong-Seok Lee, Touradj Ebrahimi
ACL
2010
13 years 5 months ago
Using Speech to Reply to SMS Messages While Driving: An In-Car Simulator User Study
Speech recognition affords automobile drivers a hands-free, eyes-free method of replying to Short Message Service (SMS) text messages. Although a voice search approach based on te...
Yun-Cheng Ju, Tim Paek
INTERSPEECH
2010
13 years 2 months ago
HMM adaptation using linear spline interpolation with integrated spline parameter training for robust speech recognition
We recently proposed a method for HMM adaptation to noisy environments called Linear Spline Interpolation (LSI). LSI uses linear spline regression to model the relationship betwee...
Michael L. Seltzer, Alex Acero
ICASSP
2011
IEEE
12 years 11 months ago
Comparing multilayer perceptron to Deep Belief Network Tandem features for robust ASR
In this paper, we extend the work done on integrating multilayer perceptron (MLP) networks with HMM systems via the Tandem approach. In particular, we explore whether the use of D...
Oriol Vinyals, Suman V. Ravuri