arXiv subjects

Mingchi Hou

Publications and source records attributed to Mingchi Hou.

4 recordsLinked to original sources

arXiv2026

Data Augmentation for Pathological Speech Enhancement

The performance of state-of-the-art speech enhancement (SE) models considerably degrades for pathological speech due to atypical acoustic characteristics and limited data availability. This paper systematically investigates data augmentation (DA) strategies to improve SE performance for pathological speakers affected by Parkinson`s disease, evaluating both predictive and generative SE models. We examine three DA categories, i.e., transformative, generative, and noise augmentation, assessing their impact with objective SE metrics. Experimental results show that noise augmentation consistently delivers the largest and most robust gains, transformative augmentations provide moderate improvements, while generative augmentation yields limited benefits and can harm performance as the amount of synthetic data increases. Furthermore, we show that the effectiveness of DA varies depending on the SE model, with DA being more beneficial for predictive SE models. While our results demonstrate that DA improves SE performance for pathological speakers, a performance gap between neurotypical and pathological speech persists, highlighting the need for future research on targeted DA strategies for pathological speech.

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arXiv2025

Influence of Clean Speech Characteristics on Speech Enhancement Performance

Speech enhancement (SE) performance is known to depend on noise characteristics and signal to noise ratio (SNR), yet intrinsic properties of the clean speech signal itself remain an underexplored factor. In this work, we systematically analyze how clean speech characteristics influence enhancement difficulty across multiple state of the art SE models, languages, and noise conditions. We extract a set of pitch, formant, loudness, and spectral flux features from clean speech and compute correlations with objective SE metrics, including frequency weighted segmental SNR and PESQ. Our results show that formant amplitudes are consistently predictive of SE performance, with higher and more stable formants leading to larger enhancement gains. We further demonstrate that performance varies substantially even within a single speaker's utterances, highlighting the importance of intraspeaker acoustic variability. These findings provide new insights into SE challenges, suggesting that intrinsic speech characteristics should be considered when designing datasets, evaluation protocols, and enhancement models.

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arXiv2025

Generalizability of Predictive and Generative Speech Enhancement Models to Pathological Speakers

State of the art speech enhancement (SE) models achieve strong performance on neurotypical speech, but their effectiveness is substantially reduced for pathological speech. In this paper, we investigate strategies to address this gap for both predictive and generative SE models, including i) training models from scratch using pathological data, ii) finetuning models pretrained on neurotypical speech with additional data from pathological speakers, and iii) speaker specific personalization using only data from the individual pathological test speaker. Our results show that, despite the limited size of pathological speech datasets, SE models can be successfully trained or finetuned on such data. Finetuning models with data from several pathological speakers yields the largest performance improvements, while speaker specific personalization is less effective, likely due to the small amount of data available per speaker. These findings highlight the challenges and potential strategies for improving SE performance for pathological speakers.

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arXiv2025

Variational Autoencoder for Personalized Pathological Speech Enhancement

The generalizability of speech enhancement (SE) models across speaker conditions remains largely unexplored, despite its critical importance for broader applicability. This paper investigates the performance of the hybrid variational autoencoder (VAE)-non-negative matrix factorization (NMF) model for SE, focusing primarily on its generalizability to pathological speakers with Parkinson's disease. We show that VAE models trained on large neurotypical datasets perform poorly on pathological speech. While fine-tuning these pre-trained models with pathological speech improves performance, a performance gap remains between neurotypical and pathological speakers. To address this gap, we propose using personalized SE models derived from fine-tuning pre-trained models with only a few seconds of clean data from each speaker. Our results demonstrate that personalized models considerably enhance performance for all speakers, achieving comparable results for both neurotypical and pathological speakers.

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