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Exploiting a tissue-conductive sensor – a stethoscopic microphone – the system developed at NAIST which converts Non-Audible Murmur (NAM) to audible speech by GMM-based statistical mapping is a very promising technique. The quality of the converted speech is however still insufficient for computer-mediated communication, notably because of the poor(More)
Acoustic speaker diarization is investigated for situations where a collection of shows from the same source needs to be processed. In this case, the same speaker should receive the same label across all shows. We compare different architectures for cross-show speaker diarization: the obvious concatenation of all shows, a hybrid system combining first a(More)
Although the segmental intelligibility of converted speech from silent speech using direct signal-to-signal mapping proposed by Toda et al. [1] is quite acceptable, listeners have sometimes difficulty in chunking the speech continuum into meaningful words due to incomplete phonetic cues provided by output signals. This paper studies another approach(More)
Non-audible murmur (NAM) is an unvoiced speech received through body tissue using special acoustic sensors (i.e., NAM microphones) attached behind the talkers ear. Although NAM has different frequency characteristics compared to normal speech, it is possible to perform automatic speech recognition (ASR) using conventional methods. In using a NAM microphone,(More)
  • Ben Atef, Viet-Anh Youssef, Pierre Tran, Gérard Badin, Bailly, atef Ben-Youssef +3 others
  • 2010
Two speech inversion methods are implemented and compared. In the first, multistream Hidden Markov Models (HMMs) of phonemes are jointly trained from synchronous streams of articulatory data acquired by EMA and speech spectral parameters; an acoustic recognition system uses the acoustic part of the HMMs to deliver a phoneme chain and the states durations;(More)
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