This study investigates the efficacy of utilizing embedding spaces to model phonetic information in emotion utterances for speech emotion recognition. Our approach involves implicit modeling of phone information by deriving phone-based embeddings from netw ...
Customer Satisfaction (CS) in call centers influences customer loyalty and the company's reputation. Traditionally, CS evaluations were conducted manually or with classical machine learning algorithms; however, advancements in deep learning have led to aut ...
International Speech Communication Association2024
This study analyzes formant transitions in six English stop-consonants in vowel-consonant-vowel (VCV) sequences. We investigate whether natural speech preserves formant patterns, and if not, how it affects stop-consonant perception and automatic classifica ...
Fine-tuning has become a norm to achieve state-of-the-art performance when employing pre-trained networks like foundation models. These models are typically pre-trained on large-scale unannotated data using self-supervised learning (SSL) methods. The SSL-b ...
Institute of Electrical and Electronics Engineers Inc.2025
This study examines the acoustic features of Parkinson's patients with depression. More specifically, the research investigates whether interpretable, handcrafted acoustic feature-based methods, previously used for automatic speech-based depression detecti ...
Institute of Electrical and Electronics Engineers Inc.2025
In this work, we investigate Speech Foundation Models (SFMs) for Parkinson's Disease (PD) detection. We explore two main approaches: (1) using SFMs as frozen feature extractors and, (2) fine-tuning/adapting SFMs for PD detection. We propose a cross-validat ...
Institute of Electrical and Electronics Engineers Inc.2025