Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations

Perceptual voice quality assessment is essential for diagnosing and monitoring voice disorders by providing standardized evaluations of vocal function. Traditionally, expert raters use standard scales such as the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS). However, these metrics are subjective and prone to inter-rater variability, motivating the need for automated, objective assessment methods. This study proposes Voice Quality Assessment Network (VOQANet), a deep learning-based framework with an attention mechanism that leverages a Speech Foundation Model (SFM) to extract high-level acoustic and prosodic information from raw speech. To enhance robustness and interpretability, we also introduce VOQANet+, which integrates low-level speech descriptors such as jitter, shimmer, and harmonics-to-noise ratio (HNR) with SFM embeddings into a hybrid representation. Unlike prior studies focused only on vowel-based phonation (PVQD-A subset) of the Perceptual Voice Quality Dataset (PVQD), we evaluate our models on both vowel-based and sentence-level speech (PVQD-S subset) to improve generalizability. Results show that sentence-based input outperforms vowel-based input, especially at the patient level, underscoring the value of longer utterances for capturing perceptual voice attributes. VOQANet consistently surpasses baseline methods in root mean squared error (RMSE) and Pearson correlation coefficient (PCC) across CAPE-V and GRBAS dimensions, with VOQANet+ achieving even better performance. Additional experiments under noisy conditions show that VOQANet+ maintains high prediction accuracy and robustness, supporting its potential for real-world and telehealth deployment.

Towards Robust Automated Perceptual Voice Quality Assessment with Speech Foundation Models

Perceptual voice quality assessment is essential for diagnosing and monitoring voice disorders. Traditionally, expert raters use scales such as the CAPE-V and GRBAS. However, these are subjective and prone to inter-rater variability, motivating the need for automated, objective assessment methods. This study proposes VOQANet, a deep learning framework with an attention mechanism that leverages a Speech Foundation Model (SFM) to extract high-level acoustic and prosodic information from raw speech. To improve robustness and interpretability, we introduce VOQANet+, which integrates handcrafted acoustic features such as jitter, shimmer, and harmonics-to-noise ratio (HNR) with SFM embeddings into a hybrid representation. Unlike prior work focusing only on vowel-based phonation (PVQD-A subset) from the Perceptual Voice Quality Dataset (PVQD), we evaluate our models on both vowel-based and sentence-level speech (PVQD-S subset) for better generalizability. Results show that sentence-based input outperforms vowel-based input, particularly at the patient level, highlighting the benefit of longer utterances for capturing voice attributes. VOQANet consistently surpasses baseline methods in root mean squared error and Pearson correlation across CAPE-V and GRBAS dimensions, with VOQANet+ achieving further improvements. Additional tests under noisy conditions show that VOQANet+ maintains high prediction accuracy, supporting its use in real-world and telehealth settings. These findings demonstrate the value of combining SFM embeddings with domain-informed acoustic features for interpretable and robust voice quality assessment.

Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.