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Speech Playground is an interactive tool for speech analysis and comparison, combining a Python backend with a web frontend to explore multiple feature types and support utterance comparison for research and computer-aided pronunciation training.
This paper proposes a Multi-View Gated Graph Attention Network for Alzheimer's Disease detection from spontaneous speech, using semantic, dependency, and co-occurrence graphs with an adaptive gated fusion mechanism. The model achieves 90.00% accuracy on the ADReSSo dataset, and the source code is publicly available.
This paper presents a multi-stage explainable framework that combines SHAP-based token attribution, theory-informed linguistic features, and LLaMA-3.1-70B-Instruct LLM reasoning to interpret transformer-based speech models for cognitive impairment detection, achieving strong clinical alignment and high usability scores.
This paper investigates whether everyday speech from older adults can be used as a personalized cognitive monitoring tool, finding that AI models can detect subtle language patterns indicative of cognitive decline, unlike standard GPT responses.
Proposes a multimodal framework for fair Mild Cognitive Impairment detection from speech, using unlearning via gradient reversal to reduce demographic bias and improve performance across subgroups.
This paper introduces MA-DLE, a memory-based feature augmentation method for speech-based automatic depression level estimation, achieving state-of-the-art performance on the DAIC-WOZ and E-DAIC datasets.
This paper proposes a cross-linguistic transfer learning approach for detecting Alzheimer's Disease from speech across multiple languages, achieving F1 scores of 82% and supporting real-time screening applications.
This academic paper investigates using LLMs for zero-shot prediction of psychological well-being scores from spontaneous speech, evaluating 12 models and achieving high correlation with clinical metrics.