Colloque avec actes et comité de lecture. nationale.National audienceIn real world applications, speech recognition is confronted with two main difficulties: the non native speakers and the background noise. The aim of this paper is to compare on the same noisy database different methods in order to increase the robustness of our HMM-based automatic speech recognition system. To deal with the non native speakers, we have tested two solutions: multi-models and adaptation techniques. For noisy speech, we have evaluated two types of methods: the first one (PMC and MLLR) adapts the initial models, trained in clean speech, with a few noisy sentences. The second one (RATZ and MCR) tries to remove the noise from the signal without modifying the HM...
A challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy offi...
This paper presents a multiple-model framework for noise-robust speech recognition. In this framewor...
Automatic speech recognition (ASR) systems frequently work in a noisy environment. As they are often...
Colloque avec actes et comité de lecture. internationale.International audienceHidden Markov models ...
EUROSPEECH2003: 8th European Conference on Speech Communication and Technology, September 1-4, 2003...
EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, ...
In this paper, experiments were performed to evaluate the principal performance boundaries of adapte...
A challenging scenario is addressed in which a distant-talking speech recognizer operates in a noisy...
Colloque avec actes et comité de lecture. internationale.International audienceNoise degrades the pe...
Challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy office...
Abstract. Discriminatively trained HMMs are investigated in both clean and noisy environments in thi...
This paper addresses the problem of hands-free speech recognition in a noisy office environment. An ...
In this paper a challenging scenario is addressed in which a hands-free speech recognizer operates i...
Abstract The highest recognition performance is still achieved when training a recognition system wi...
Abstract. Most of current speech recognition systems are based on Hidden Markov Models assuming that...
A challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy offi...
This paper presents a multiple-model framework for noise-robust speech recognition. In this framewor...
Automatic speech recognition (ASR) systems frequently work in a noisy environment. As they are often...
Colloque avec actes et comité de lecture. internationale.International audienceHidden Markov models ...
EUROSPEECH2003: 8th European Conference on Speech Communication and Technology, September 1-4, 2003...
EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, ...
In this paper, experiments were performed to evaluate the principal performance boundaries of adapte...
A challenging scenario is addressed in which a distant-talking speech recognizer operates in a noisy...
Colloque avec actes et comité de lecture. internationale.International audienceNoise degrades the pe...
Challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy office...
Abstract. Discriminatively trained HMMs are investigated in both clean and noisy environments in thi...
This paper addresses the problem of hands-free speech recognition in a noisy office environment. An ...
In this paper a challenging scenario is addressed in which a hands-free speech recognizer operates i...
Abstract The highest recognition performance is still achieved when training a recognition system wi...
Abstract. Most of current speech recognition systems are based on Hidden Markov Models assuming that...
A challenging scenario is addressed in which a hands-free speech recognizer operates in a noisy offi...
This paper presents a multiple-model framework for noise-robust speech recognition. In this framewor...
Automatic speech recognition (ASR) systems frequently work in a noisy environment. As they are often...