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AI Research
arXiv CS.AI·May 26·5 min read
Zero-Shot Parkinson's Disease Detection from Speech: Comparing Large Audio and Language Models
TL;DR
Parkinson's disease (PD) detection from speech is challenging, particularly regarding the input format used. This study compares two methods: handcrafted acoustic features analyzed by a general-purpose language model and raw audio waveforms analyzed by audio-capable models.
✦ Why It Matters
Engineers can enhance PD detection systems by choosing the appropriate audio input method based on language and resource availability.
Key Takeaways
- Explore different input modalities for speech analysis.
- Consider language resource availability when developing models.
- Optimize models based on dataset characteristics.
- Utilize findings to enhance diagnostic tools for PD.
Full Summary
Detecting Parkinson's disease (PD) through speech analysis is a promising area of research, but the effectiveness of different audio input types remains unclear. This study systematically compares two approaches for zero-shot PD detection: using handcrafted acoustic features with a general-purpose language model (LLM) and analyzing raw audio waveforms with audio-capable models.
Experiments were conducted on PD speech datasets in four languages, revealing that the choice of input modality significantly impacts detection performance. Handcrafted features provided consistent results in low-resource languages like Bengali, while raw audio input yielded variable results based on the specific dataset used.
These findings suggest that the input format can influence the effectiveness of AI models in medical diagnostics. Understanding these differences can help researchers optimize models for diverse languages and contexts.