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The results of our research presented in this paper is twofold. First, an estimation of global posteriors is formalized in the framework of hybrid HMM/ANN systems. It is shown that hybrid HMM/ANN systems, in which the ANN part estimates local posteriors, can be used to modelize global model posteriors. This for-malization provides us with a clear theory in(More)
In this paper, we propose a new acoustic confidence measure of ASR hypothesis and compare it to approaches proposed in the literature. This approach takes into account prior information on the acoustic model performance specific to each phoneme. The new method is tested on two types of recognition errors: the out-of-vocabulary words and the errors due to(More)
This paper intends to summarize recent developments and experimental results related to Automatic Speech Recognition (ASR) using signals captured with a throat-microphone. Due to the proximity of the sensor to the voice source, the signal is naturally less subject to background noise. This however yields speech sounds that have different frequency contents(More)
The paper proposes a solution that brings some advances to the genericity of the ASR technology towards tasks and languages. A non-linear discriminant model is built from multilingual , multi-task speech material in order to classify the acoustic signal into language independent phonetic units. Instead of considering this model for direct HMM state(More)
Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background(More)