Abstract
The analysis of speech onset times has a longstanding tradition in experimental psychology as a measure of how a stimulus influences a spoken response. Yet the lack of accurate automatic methods to measure such effects forces researchers to rely on time-intensive manual or semiautomatic techniques. Here we present Chronset, a fully automated tool that estimates speech onset on the basis of multiple acoustic features extracted via multitaper spectral analysis. Using statistical optimization techniques, we show that the present approach generalizes across different languages and speaker populations, and that it extracts speech onset latencies that agree closely with those from human observations. Finally, we show how the present approach can be integrated with previous work (Jansen & Watter Behavior Research Methods, 40:744–751, 2008) to further improve the precision of onset detection. Chronset is publicly available online at www.bcbl.eu/databases/chronset.
Highlights
The analysis of speech onset times has a longstanding tradition in experimental psychology as a measure of how a stimulus influences a spoken response
We show how our approach can be combined with the SayWhen algorithm to achieve estimates of speech onset that are even closer to human rater precision
Agreement between human ratings for the speech recordings in the Spanish dataset We first examined the correspondence between the handcoded onset latencies calculated using a semi-automatic method by the two human raters for vocal responses in the Spanish dataset (Fig. 2a). This revealed that the hand-coded onset latencies were highly consistent and strongly correlated between the human raters (ICC = .99, R2 = .99, offset = 0.95 ms)
Summary
The analysis of speech onset times has a longstanding tradition in experimental psychology as a measure of how a stimulus influences a spoken response. The current gold standard is to rely on human raters, often aided by semi-automatic rating techniques (Jansen & Watter, 2008; Protopapas, 2007). To date, this approach has yielded some of the most accurate and consistent estimates of speech onsets. In the majority of cases voice keys fail to achieve accurate measurements whenever vocal responses are preceded by loud noise sounds or are characterized by complex acoustic onsets (Kessler, Treiman, & Mullennix, 2002; Rastle & Davis, 2002), both of which are frequent occurrences in natural speech
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