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An unsupervised quantitative measure for word prominence in spontaneous speech

An unsupervised approach for automatic speech prominence detection is proposed in this paper. The algorithm scores prominence by fusing different acoustic feature sets from the speech signal correlation envelope. In addition, we investigate part of speech (POS) as a linguistic correlate for speech prominence. We also underscore the inadequacy of the traditional approach to prominence detection of heuristically tagging speech prominence into discrete levels (categories). Instead, we propose to keep the prominence score continuous, evaluate it by correlation with POS, and leave it for further processing by other applications such as natural language understanding. Furthermore, in contrast to most previous studies, we evaluate prominence scoring on spontaneous speech data (switchboard corpus). Our experimental results indicate that the proposed prominence score can robustly distinguish between content word and function word classes.