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Using automated syllable counting to detect missing information in speech transcripts from clinical settings.

Marama Diaz-Asper¹, Terje B Holmlund², Chelsea Chandler³

¹Washington International School, Washington DC, United States.

Psychiatry Research

|July 15, 2022

Summary

Automated transcription accuracy is impacted by audio quality and speaker traits. An automated syllable counter can quantify speech omissions, especially in low-quality recordings, aiding clinical applications.

Keywords:

Automatic speech recognition Syllables Word error rate

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Area of Science:

Speech analysis
Clinical informatics
Biomedical engineering

Background:

Speech rate and quantity are key indicators of clinical status.
Automated transcription offers potential for clinical monitoring and analysis.
Challenges exist in transcription accuracy due to recording quality and speaker variability.

Purpose of the Study:

To investigate the impact of recording quality and speaker characteristics on automated transcription accuracy.
To evaluate an automated syllable counting method for quantifying speech omissions.
To assess the utility of syllable counting in identifying transcription errors in challenging clinical audio.

Main Methods:

Utilized two datasets with varying recording quality and speaker attributes.
Employed an automated syllable counter to estimate speech output and measure omissions.
Correlated syllable count discrepancies with word error rates in transcripts.

Main Results:

Low-quality recordings led to significant omissions in automated transcripts.
The automated syllable counter effectively estimated speech output and quantified missing information.
The correlation between missing syllables and word error rate was significant only under low audio quality conditions.

Conclusions:

Automated syllable counting can quantify speech omissions in transcription.
This method is particularly valuable in clinical settings with problematic audio quality or diverse speaker characteristics.
Syllable counting aids in flagging transcription omissions, enhancing the reliability of automated speech analysis for clinical state assessment.