PathBench: Speech Intelligibility Benchmark for Automatic Pathological Speech Assessment
Abstract
Automatic speech intelligibility assessment is crucial for monitoring speech disorders and therapy efficacy. However, existing methods are difficult to compare: research is fragmented across private datasets with inconsistent protocols. We introduce PathBench, a unified benchmark for pathological speech assessment using public datasets. We compare reference-free, reference-text, and reference-audio methods across three protocols (Matched Content, Extended, and Full) representing how a linguist (controlled stimuli) versus machine learning specialist (maximum data) would approach the same data. We establish benchmark baselines across six datasets, enabling systematic evaluation of future methodological advances, and introduce Dual-ASR Articulatory Precision (DArtP), achieving the highest average correlation among reference-free methods.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents PathBench, a comprehensive benchmarking framework for assessing speech intelligibility in pathological speech, addressing critical gaps in the field. The innovative methodology and rigorous experimental evaluation contribute significantly to advancing the state of research in automatic speech assessment, with potential applications in clinical settings.
Methodology
The paper introduces PathBench, a systematic benchmarking framework for assessing speech intelligibility in pathological speech, which is a significant advancement given the fragmented nature of existing research. The methodology is robust, employing a variety of protocols that cater to both linguistic and machine learning perspectives. The introduction of Dual-ASR Articulatory Precision (DArtP) as a reference-free method is particularly innovative, providing a new way to evaluate articulatory precision without the need for labeled training data. The authors also address confounding factors such as speaker age and recording noise, which enhances the credibility of their findings.
Results & evaluation
The experiments are comprehensive, utilizing six datasets and establishing baseline performances across multiple protocols. The results demonstrate that DArtP achieves the highest correlation among reference-free methods, which is a notable contribution. The statistical analyses, including Wilcoxon Signed-Rank Tests, are well-executed, providing strong evidence for the superiority of certain methodologies over others. The detailed reporting of results across various conditions adds to the rigor of the evaluation.
Reproducibility
The authors provide a GitHub repository with code and resources, which is essential for reproducibility. However, the paper could benefit from more detailed descriptions of the datasets and specific implementation details to facilitate easier replication of the results by other researchers.
Limitations
The study is limited to four languages (English, Italian, Spanish, and Dutch), which may restrict its applicability to a broader audience. Additionally, while the authors address confounding factors, the impact of noise in real-world scenarios remains untested, which is critical for clinical applications. The reliance on public datasets may also introduce variability that could affect the generalizability of the findings.
Broader impact
The implications of this research are significant for the fields of speech therapy and clinical assessment of speech disorders. By providing a standardized benchmarking framework, PathBench can facilitate future research and development of more effective speech intelligibility assessment tools. This could ultimately improve patient outcomes in clinical settings by enabling better monitoring and evaluation of speech disorders. The paper presents PathBench, a comprehensive benchmarking framework for assessing speech intelligibility in pathological speech, addressing critical gaps in the field. The innovative methodology and rigorous experimental evaluation contribute significantly to advancing the state of research in automatic speech assessment, with potential applications in clinical settings.
Author of this paper? Send a correction.