An evaluation of machine learning methods for prominence detection in French

The automatic detection of prosodically prominent syllables is crucial for analysing speech, especially in French where prominence contributes substantially to prosodic grouping and boundary demarcation. In this paper, we compare different machine learning techniques for the automatic detection of prominent syllables, using prosodic features (including pitch, energy, duration and spectral balance) and lexical information. We explore the differences between modelling the detection of prominent syllables as a classification or as a sequence labelling problem, and combinations of the two techniques. We train and evaluate our systems on a corpus of spontaneous French speech, consisting of almost 100 different speakers; the corpus is balanced for speaker age and sex and covers 3 different regional varieties. The result of this study is a novel tool for the automatic annotation of prominent syllables in French.