Scale-space expansion of acoustic features improves speech event detection

In a system for detecting and measuring phonetic events (here bursts, voice onsets, and voice-onset times), we show that the addition of features smoothed at multiple scales can improve both recall (the proportion of events correctly identified) and measurement accuracy (the timing of events and the difference between event times, relative to expert human judgments). Multi-scale (or “scale space”) features had an especially strong positive effect on robustness across datasets with different materials and recording conditions. Standard machine-learning classifiers were able to integrate information across scales, without any special treatment of the multi-scale features.