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SayWhen: An automated method for high-accuracy speech onset detection

Abstract

Many researchers across many experimental domains utilize the latency of spoken responses as a dependent measure. These measurements are typically made using a voice key, an electronic device that monitors the amplitude of a voice signal, and detects when a predetermined threshold is crossed. Unfortunately, voice keys have been repeatedly shown to be alarmingly errorful and biased in accurately detecting speech onset latencies. We present SayWhen—an easy-to-use software system for offline speech onset latency measurement that (1) automatically detects speech onset latencies with high accuracy, well beyond voice key performance, (2) automatically detects and flags a subset of trials most likely to have mismeasured onsets, for optional manual checking, and (3) implements a graphical user interface that greatly speeds and facilitates the checking and correction of this flagged subset of trials. This automatic-plus-selective-checking method approaches the gold standard performance of full manual coding in a small fraction of the time.

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Correspondence to Peter A. Jansen or Scott Watter.

Additional information

This project was supported by Natural Science and Engineering Research Council of Canada (NSERC) Grant 327454 to S.W. Our speech onset detection software is available from our Web site, cogsci.mcmaster.ca, or by contacting the authors.

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Jansen, P.A., Watter, S. SayWhen: An automated method for high-accuracy speech onset detection. Behavior Research Methods 40, 744–751 (2008). https://doi.org/10.3758/BRM.40.3.744

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  • DOI: https://doi.org/10.3758/BRM.40.3.744

Keywords

  • Speech Signal
  • Onset Detection
  • Human Attention
  • Scanning Window
  • Candidate Signal Detection