collaborators

11 papers

eess.AS2026

AMECxSV: Adaptive Metadata-Driven Embedding-Fusion Calibration for X-Lingual Speaker Verification

Xin Wei, Shi He, Yihe Yuan +3

In X-lingual automatic speaker verification (ASV), fixed front-end scores vary in reliability with language match, duration, and score source. We propose AMECxSV, an adaptive metad…

cs.AI2026

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives

Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri +1

Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech. However, the interactional timing…

eess.AS2026

Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment

Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou +2

Significant disparities exist in the diagnosis and clinical presentation of depression across different linguistic populations. Speech-based depression detection performs well mono…

eess.AS2026

Speaker-Aware Temporal Aggregation Strategies on Segment Representations for Depression Detection in Dyadic Interaction: A Benchmark Study

Anisha Pattanayak, Huang-Cheng Chou, Shrikanth Narayanan +1

Speech-based depression detection compresses features from short audio segments into one speaker-level decision, a step called temporal aggregation rarely studied on its own. Most…

eess.AS2026

Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR

Abhijit Sinha, Hemant Kumar Kathania, Sudarsana Reddy Kadiri +1

The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has d…

eess.AS2026

DSSCNet: A Transfer Learning Framework for Cross-Corpus Dysarthric Speech Severity Classification

Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota +2

Dysarthric speech severity classification is challenging due to speaker variability, class imbalance, and limited datasets. This study introduces DSSCNet, a deep learning model tha…