5 citations · 7 across the 13 of their papers we have counts for
13 papers · 1 filter
Rethinking Batch Composition: Promoting Feature Diversity for Speech Enhancement
Mingchi Hou, Ina Kodrasi
Speech enhancement systems are commonly trained using randomly sampled mini-batches, which may contain limited diversity in intrinsic acoustic characteristics of the clean speech s…
Multiclass Speech Classification Under Noise Disparity
Mahdi Amiri, Sayantan Biswas, Mingchi Hou +2
We investigate noise disparity in multi-class speech classification tasks and develop a strategy to prevent classifiers from exploiting class-dependent noise characteristics. Build…
Augmenting Large Audio Language Models with Low-Level Acoustic Features for Dysarthric Speech Detection
Mahdi Amiri, Hatef Otroshi Shahreza, Pascal Frossard +1
Automatic dysarthric speech detection approaches can support traditional clinical diagnosis, which relies on costly and time-consuming evaluation by a speech and language pathologi…
CLAP-Based Automatic Word Naming Recognition in Post-Stroke Aphasia
Yacouba Kaloga, Marina Laganaro, Ina Kodrasi
Conventional automatic word-naming recognition systems struggle to recognize words from post-stroke patients with aphasia because of disfluencies and mispronunciations, limiting re…
Data Augmentation for Pathological Speech Enhancement
Mingchi Hou, Enno Hermann, Ina Kodrasi
The performance of state-of-the-art speech enhancement (SE) models considerably degrades for pathological speech due to atypical acoustic characteristics and limited data availabil…
Generalizability of Predictive and Generative Speech Enhancement Models to Pathological Speakers
Mingchi Hou, Ante Jukic, Ina Kodrasi
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…
Influence of Clean Speech Characteristics on Speech Enhancement Performance
Mingchi Hou, Ina Kodrasi
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 a…
Towards interpretable emotion recognition: Identifying key features with machine learning
Yacouba Kaloga, Ina Kodrasi
Unsupervised methods, such as wav2vec2 and HuBERT, have achieved state-of-the-art performance in audio tasks, leading to a shift away from research on interpretable features. Howev…
Exploring In-Context Learning Capabilities of ChatGPT for Pathological Speech Detection
Mahdi Amiri, Hatef Otroshi Shahreza, Ina Kodrasi
Automatic pathological speech detection approaches have shown promising results, gaining attention as potential diagnostic tools alongside costly traditional methods. While these a…
Variational Autoencoder for Personalized Pathological Speech Enhancement
Mingchi Hou, Ina Kodrasi
The generalizability of speech enhancement (SE) models across speaker conditions remains largely unexplored, despite its critical importance for broader applicability. This paper i…
Overview of Automatic Speech Analysis and Technologies for Neurodegenerative Disorders: Diagnosis and Assistive Applications
Shakeel A. Sheikh, Md. Sahidullah, Ina Kodrasi
Advancements in spoken language technologies for neurodegenerative speech disorders are crucial for meeting both clinical and technological needs. This overview paper is vital for…
Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection
Yacouba Kaloga, Shakeel A. Sheikh, Ina Kodrasi
Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology i…
Suppressing Noise Disparity in Training Data for Automatic Pathological Speech Detection
Mahdi Amiri, Ina Kodrasi
Although automatic pathological speech detection approaches show promising results when clean recordings are available, they are vulnerable to additive noise. Recently it has been…