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Enabling Off-the-shelf Disfluency Detection And Categorization For Pathological Speech.

Home
Enabling Off-the-shelf Disfluency Detection And Categorization For Pathological Speech.

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Enabling Off-the-Shelf Disfluency Detection and Categorization for Pathological Speech.

Amrit Romana¹, Minxue Niu¹, Matthew Perez¹

¹Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.

|May 6, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study enhances speech disfluency detection and categorization, particularly for repetitions and revisions, using BERT (Bidirectional Encoder Representations from Transformers) and triplet loss. The findings show improved performance on pathological speech analysis.

Keywords:

BERT disfluencies pathological speech

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Area of Science:

Computational linguistics
Speech processing
Artificial intelligence in healthcare

Background:

Speech disfluencies, like pauses and repetitions, disrupt speech flow and can indicate health conditions, especially cognitive impairment.
Current disfluency modeling research prioritizes detection over categorization, with specific challenges in classifying repetitions and revisions.

Purpose of the Study:

To evaluate Bidirectional Encoder Representations from Transformers (BERT) against other models for speech disfluency detection and categorization.
To propose and assess a novel fine-tuning approach using triplet loss to improve the distinction between repetitions and revisions.

Main Methods:

Comparative analysis of BERT and other models on standard disfluency datasets.
Implementation of a secondary fine-tuning task for BERT utilizing triplet loss to differentiate speech repairs.

Application of fine-tuned models to analyze small corpora of pathological speech.

Main Results:

BERT, particularly with triplet loss, demonstrated superior performance in both disfluency detection and categorization compared to previous methods.
The proposed method showed significant improvements in classifying challenging categories like repetitions and revisions.
The study presents the first analysis of fine-tuning BERT on available disfluency data for off-the-shelf use on pathological speech.

Conclusions:

BERT with triplet loss offers a powerful and improved approach for speech disfluency analysis.
This method shows promise for clinical applications, aiding in the assessment of conditions like cognitive impairment through speech analysis.
The fine-tuning strategy enables effective application of advanced models to specialized pathological speech datasets.