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» Phoneme recognition using Boosted Binary Features
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ICASSP
2011
IEEE
12 years 11 months ago
Automatic speech recognition using Hidden Conditional Neural Fields
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted featu...
Yasuhisa Fujii, Kazumasa Yamamoto, Seiichi Nakagaw...
IWBRS
2005
Springer
168views Biometrics» more  IWBRS 2005»
14 years 29 days ago
Gabor Feature Selection for Face Recognition Using Improved AdaBoost Learning
Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual infor...
LinLin Shen, Li Bai, Daniel Bardsley, Yangsheng Wa...
ICASSP
2011
IEEE
12 years 11 months ago
MLP based phoneme detectors for Automatic Speech Recognition
Phoneme posterior probabilities estimated using Multi-Layer Perceptrons (MLPs) are extensively used both as acoustic scores and features for speech recognition. In this paper we e...
Samuel Thomas, Patrick Nguyen, Geoffrey Zweig, Hyn...
ICASSP
2010
IEEE
13 years 7 months ago
Boosted binary features for noise-robust speaker verification
The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a nov...
Anindya Roy, Mathew Magimai-Doss, Sébastien...
INTERSPEECH
2010
13 years 2 months ago
Learning from human errors: prediction of phoneme confusions based on modified ASR training
In an attempt to improve models of human perception, the recognition of phonemes in nonsense utterances was predicted with automatic speech recognition (ASR) in order to analyze i...
Bernd T. Meyer, Birger Kollmeier