Lamia Karray

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Recognition performance decreases when recognition systems are used over the telephone network, especially wireless network and noisy environments. It appears that non-efficient speech/non-speech detection (SND) is an important source of this degradation. Therefore, speech detection robustness to noise is a challenging problem to be examined, in order to(More)
In real-life applications, errors in the speech recognition system are mainly due to inefficient detection of speech Ž. segments, unreliable rejection of Out-Of-Vocabulary OOV words, and insufficient account of noise and transmission channel effects. In this paper, we review a set of techniques developed at CNET in order to increase the robustness to(More)
This paper deals with automatic speech recognition robust-ness for noisy wireless communications. We propose several solutions to improve speech recognition over the cellular network. Two architectures are derived for the recog-nizer. They are based on Hidden Markov Models (HMMs) adapted to adverse noise conditions. Then two more specific solutions aiming(More)
Recognition performance decreases when recognition systems are used over the telephone network, especially wireless network and noisy environments. It appears that non efficient speech/non-speech detection is a very important source of this degradation. Therefore, speech detector robustness to noise is a challenging problem to be examined, in order to(More)
Recognition performance decreases when automatic recognition systems are used over the telephone network, especially wireless network and noisy environments. Previous studies have shown that non efficient speech/non-speech detection is a very important source of this degradation. Hence, speech detector robustness to noise is highly required, in order to(More)
A new image coding technique based on an Linfinity-norm criterion and exploiting statistical properties of the reconstruction error is investigated. The original image is preprocessed, quantized, encoded, and reconstructed within a given confidence interval. Two important classes of preprocessing, namely linear prediction and iterated filterbanks, are used.(More)
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