Automatic syllable detection is an important t ask when analysing v ery large speech corpora in order to answer questions concerning prosody, r h ythm, speech r a t e, speech recognition and synthesis. In this paper a new method for automatic detection of syllable nuclei is presented. Two large spoken language corpora (PhonDatII, Verbmobil) were labelled by t hree phoneticians and t h en used to adjust the k ey parameters of the algorithm and to evaluate its error rate. Additionally, parts o f t h e corpora were used to t est the i n t er-and i n traindividual consistency of the transcribers. The e v aluation of the algorithm currently shows an error rate of 12.87% for read speech a n d 21.03% for spontaneous speech. The i n t erindividual consistency of 95.8% might be considered as an upper limit for any a u t omatic detection method.
This paper proposes two novel approaches for parameter estimation of a superpositional intonation model. These approaches present linguistic and paralinguistic assumptions for initializing a pre-existing standard method. In addition, all restrictions on the configuration of commands were eliminated. The proposed linguistic hypotheses can be based on either pitch accents or lexical stress, which give rise to two different estimation methods. These two hypotheses were validated by comparison of the estimation performance relative to two standard methods, one manual and one automatic. The results of the experiments for German, English and Spanish corpora show that the proposed methods outperform the standard ones.
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