Chronset: An automated tool for detecting speech onset.

Area of Science:

• Experimental Psychology
• Acoustic Phonetics
• Computational Linguistics

Background:

• Speech onset time analysis is crucial in experimental psychology for understanding stimulus-response dynamics.
• Current manual or semi-automatic methods for measuring speech onset are time-consuming and labor-intensive.
• A need exists for accurate, automated tools to streamline speech onset time measurement in research.

Purpose of the Study:

• To introduce Chronset, a fully automated tool for estimating speech onset times.
• To validate Chronset's accuracy and generalizability across diverse languages and speaker populations.
• To demonstrate Chronset's potential for integration with existing methods to enhance precision.

Main Methods:

• Chronset utilizes multitaper spectral analysis to extract multiple acoustic features.
• Statistical optimization techniques are employed for robust speech onset estimation.
• The tool's performance is compared against human observations for validation.

Main Results:

• Chronset accurately estimates speech onset latencies, showing close agreement with human measurements.
• The automated approach demonstrates generalizability across different languages and speaker populations.
• Integration with prior work further refines the precision of speech onset detection.

Conclusions:

• Chronset offers a reliable and efficient automated solution for speech onset time analysis.
• The tool significantly reduces the time and effort required for speech response measurement.
• Chronset is publicly available, facilitating its adoption in experimental psychology and related fields.