17 accepted papers
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) trainin…
Despite the promising performance of state-of-the-art approaches for Parkinson’s Disease (PD) detection, these approaches often analyze individual speech segments in isolation, which can lead to sub-optimal results. Dysarthric cues that characterize speech impairments from PD patients are expected t…
Low-rank adaptation (LoRA) is a widely used method for parameter-efficient finetuning. However, existing LoRA variants lack mechanisms to explicitly disambiguate task-relevant information within the learned low-rank subspace, potentially limiting downstream performance. We propose Factorized Variat…
Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology-irrelevant uncorrelated information, such as changing phonetic content or variations in speaking style across time, which…
Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthr…
Mainstream deep learning-based dysarthric speech detection approaches typically rely on processing the magnitude spectrum of the short-time Fourier transform of input signals, while ignoring the phase spectrum. Although considerable insight about the structure of a signal can be obtained from the ma…
Automatic techniques in the context of motor speech disorders (MSDs) are typically two-class techniques aiming to discriminate between dysarthria and neurotypical speech or between dysarthria and apraxia of speech (AoS). Further, although such techniques are proposed to support the perceptual assess…
Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of pairwise distance matrices using conv…
Automatic pathological speech intelligibility measures are crucial to assist the clinical diagnosis and treatment of speech disorders. The recently proposed pathological short-time objective intelligibility (P-ESTOI) measure was shown to be very advantageous, yielding a high performance for several…
The multi-channel Wiener filter (MWF) is a well-known multi-microphone speech enhancement technique, aiming at improving the quality of the recorded speech signals in noisy and reverberant environments. Assuming that reverberation and ambient noise can be modeled as a diffuse sound field and the spa…
Impaired speech intelligibility in motor speech disorders arising due to neurological diseases negatively affects the communication ability and quality of life of patients. Reliable and cost-effective measures to automatically assess speech intelligibility are necessary for the management of such di…
Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are progressive neurodegenerative diseases which, among other symptoms, cause dysarthria of speech. To assist the clinical diagnosis and treatment of neurological diseases, several studies have addressed the characterization and classi…
In noisy and reverberant environments speech enhancement techniques such as the multi-channel Wiener filter (MWF) can be used to improve speech quality and intelligibility. Assuming that reverberation and ambient noise can be modeled as diffuse sound fields, such techniques require an estimate of th…
Many multi-channel dereverberation and noise reduction techniques such as the multi-channel Wiener filter (MWF) require an estimate of the late reverberation and noise power spectral densities (PSDs). State-of-the-art multi-channel methods for estimating the late reverberation PSD typically assume t…
Multi-channel methods for estimating the late reverberant power spectral density (PSD) rely on an estimate of the direction of arrival (DOA) of the speech source or of the relative early transfer functions (RETFs) of the target signal from a reference microphone to all microphones. The DOA and the R…
This paper presents a novel signal-dependent method to increase the robustness of acoustic multi-channel equalization techniques against room impulse response (RIR) estimation errors. Aiming at obtaining an output signal which better resembles a clean speech signal, we propose to extend the acoustic…
The objective of the speech distortion weighted multichannel Wiener filter (MWF) is to reduce background noise while controlling speech distortion. This can be achieved by means of a trade-off parameter, hence, selecting an optimal trade-off parameter is of crucial importance. Aiming at incorporatin…