The goal of this paper is to investigate French word segmentation strategies using phonemic and lexical transcriptions as well as prosodic and part-of-speech annotations. Average fundamental frequency (f0) profiles and phoneme duration profiles are measured using 13 hours of broadcast news speech to study prosodic regularities of French words. Some influential factors are taken into consideration for f0 and duration measurements: word syllable length, word-final schwa, part-of-speech. Results from average f0 profiles confirm word final syllable accentuation and from average duration profiles, we can observe long word final syllable length. Both are common tendencies in French. From noun phrase studies, results of average f0 profiles illustrate higher noun first syllable after determiner. Inter-vocalic duration profile results show long inter-vocalic duration between determiner vowel and preceding word vowel. These results reveal measurable cues contributing to word boundary location. Further studies will include more detailed within syllable f0 patterns, other speaking styles and languages.

The present contribution aims at increasing our understanding of automatic speech recognition (ASR) errors involving frequent homophone or almost homophone words by confronting them to perceptual results. The long-term aim is to improve acoustic modelling of these items to reduce automatic transcription errors. A first question of interest addressed in this paper is whether homophone words such as “et” (and); and “est” (to be), for which ASR systems rely on language model weights, can be discriminated in a perceptual transcription test with similar n-gram constraints. A second question concerns the acoustic separability of the two homophone words using appropriate acoustic and prosodic attributes. The perceptual test reveals that even though automatic and perceptual errors correlate positively, human listeners deal with local ambiguity more efficiently than the ASR system in conditions which attempt to approximate the information available for decision for a 4-gram language model. The corresponding acoustic analysis shows that the two homophone words may be distinguished thanks to some relevant acoustic and prosodic attributes. A first experiment in automatic classification of the two words using data mining techniques highlights the role of the prosodic (duration and voicing) and contextual information (pauses co-occurrence) in distinguishing the two words. Current results, even though preliminary, suggests that new levels of information, so far unexplored in pronunciations’ modelling for ASR, may be considered in order to efficiently factorize the word variants observed in speech and to improve the automatic speech transcription.