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» Corrected tandem features for acoustic model training
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COLING
2010
13 years 2 months ago
Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets
An unsupervised discriminative training procedure is proposed for estimating a language model (LM) for machine translation (MT). An English-to-English synchronous context-free gra...
Zhifei Li, Ziyuan Wang, Sanjeev Khudanpur, Jason E...
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...
DSP
2007
13 years 7 months ago
Feasibility of impact-acoustic emissions for detection of damaged wheat kernels
A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resu...
Tom C. Pearson, A. Enis Çetin, Ahmed H. Tew...
ICASSP
2011
IEEE
12 years 11 months ago
A paired test for recognizer selection with untranscribed data
Traditionally, the use of untranscribed speech has been restricted to unsupervised or semi-supervised training of acoustic models. Comparison of recognizers has required labeled d...
Bhiksha Raj, Rita Singh, James Baker
INTERSPEECH
2010
13 years 2 months ago
Improved language recognition using mixture components statistics
One successful approach to language recognition is to focus on the most discriminative high level features of languages, such as phones and words. In this paper, we applied a simi...
Abualsoud Hanani, Michael J. Carey 0002, Martin J....