Characterization of Arabic sibilant consonants

The aim of this study is to develop an automatic speech recognition system in order to classify sibilant Arabic consonants into two groups: alveolar consonants and post-alveolar consonants. The proposed method is based on the use of the energy distribution, in a consonant-vowel type syllable, as an acoustic cue. The application of this method on our own corpus reveals that the amount of energy included in a vocal signal is a very important parameter in the characterization of Arabic sibilant consonants. For consonants classifications, the accuracy achieved to identify consonants as alveolar or post-alveolar is 100%. For post-alveolar consonants, the rate is 96% and for alveolar consonants, the rate is over 94%. Our classification technique outperformed existing algorithms based on support vector machines and neural networks in terms of classification rate

Segmental intonation information in French fricatives

International audienceWe examined the "segmental intonation" hypothesis (Niebuhr, 2012), according to which voiceless consonants contain spectral information that may contribute to the percept of high or low pitch in the absence of fundamental frequency (F0). French speakers read target words embedded in a carrier phrase and containing fricatives in accentual phrase-initial,-medial or-final position (e.g. sidéré 'stunned', nécessite 'require', ressaisisse 'seize again'), expected to correspond to regions of low, intermediate or high F0, respectively, as well as control words containing only sonorants (e.g. laminé 'rolled'). Analyses show lower center of gravity (CoG) for word-initial (low F0 region) than for word-final (high F0 region) fricatives. For word-final fricatives, CoG is higher at the end than in the beginning of the fricative, which may contribute to the percept of the continuation of the F0 rise across the preceding vowel.ReferenceNiebuhr, Oliver. 2012. At the edge of intonation – The interplay of utterance-final F0 movements and voiceless fricative sounds. Phonetica 69, 7–27

Wavelet-based techniques for speech recognition

In this thesis, new wavelet-based techniques have been developed for the extraction of features from speech signals for the purpose of automatic speech recognition (ASR). One of the advantages of the wavelet transform over the short time Fourier transform (STFT) is its capability to process non-stationary signals. Since speech signals are not strictly stationary the wavelet transform is a better choice for time-frequency transformation of these signals. In addition it has compactly supported basis functions, thereby reducing the amount of computation as opposed to STFT where an overlapping window is needed. [Continues.