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キーワード:
Dutch
Prosody
Lexical stress
Automatic vowel classification
Automatic speech recognition
要旨:
In this paper we investigate acoustic differences between vowels in syllables that do or do not carry lexical stress. In doing so, we concentrated on segmental acoustic phonetic features that are conventionally assumed to differ between stressed and unstressed syllables, viz. Duration, Energy and Spectral Tilt. The speech material in this study differs from the type of material used in previous research: instead of specially constructed sentences we used phonetically rich sentences from the Dutch POLYPHONE corpus. Most of the Duration, Energy and Spectral Tilt features that we used in the investigation show statistically significant differences for the population means of stressed and unstressed vowels. However, it also appears that the distributions overlap to such an extent that automatic detection of stressed and unstressed syllables yields correct classifications of 72.6% at best. It is argued that this result is due to the large variety in the ways in which the abstract linguistic feature `lexical stress' is realized in the acoustic speech signal. Our findings suggest that a lexical stress detector has little use for a single pass decoder in an automatic speech recognition (ASR) system, but could still play a useful role as an additional knowledge source in a multi-pass decoder.
