Abstract
Several automatic phonetic alignment tools have been proposed in the literature. They generally use speaker-independent acoustic models of the language to align new corpora. The problem is that the range of provided models is limited. It does not cover all languages and speaking styles (spontaneous, expressive, etc.). This study investigates the possibility of directly training the statistical model on the corpus to align. The main advantage is that it is applicable to any language and speaking style. Moreover, comparisons indicate that it provides as good or better results than using speaker-independent models of the language. It shows that about 2% are gained, with a 20 ms threshold, by using our method. Experiments were carried out on neutral and expressive corpora in French and English. The study also points out that even a small neutral corpus of a few minutes can be exploited to train a model that will provide high-quality alignment.
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Brognaux, S., Roekhaut, S., Drugman, T., Beaufort, R. (2012). Automatic Phone Alignment. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_30
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DOI: https://doi.org/10.1007/978-3-642-33983-7_30
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