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» Large-Vocabulary Speech Recognition Algorithms
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79
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ICASSP
2008
IEEE
15 years 5 months ago
Multilingual weighted codebooks
In this paper we present an approach for speech recognition of multiple languages with constrained resources on embedded devices. Examples of such systems are navigation systems, ...
Martin Raab, Rainer Gruhn, Elmar Nöth
93
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CSL
2010
Springer
14 years 11 months ago
Active learning and semi-supervised learning for speech recognition: A unified framework using the global entropy reduction maxi
We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select...
Dong Yu, Balakrishnan Varadarajan, Li Deng, Alex A...
NAACL
1994
15 years 11 days ago
Signal Processing for Robust Speech Recognition
This paper describes several new cepstral-based compensation procedures that render the SPHINX-II system more robust with respect to acoustical environment. The first algorithm, p...
Fu-Hua Liu, Pedro J. Moreno, Richard M. Stem, Alej...
CVPR
2012
IEEE
13 years 1 months ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
ISCAS
2003
IEEE
124views Hardware» more  ISCAS 2003»
15 years 4 months ago
Improving the filter bank of a classic speech feature extraction algorithm
The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (mfcc) algorithm. Introduced in 1980,...
Mark D. Skowronski, John G. Harris