@misc{9226135,
  abstract     = {{Speech has become an increasingly important source of non-invasive information for Parkinson's disease detection, but much existing work still relies on sustained vowels. This thesis investigates whether carefully represented continuous speech can provide added predictive value over a strong sustained-vowel benchmark. Using the NeuroVoz corpus, sustained-vowel recordings were first used to establish a controlled single-vowel reference condition.

The continuous-speech framework was built around selected vowel-centred regions rather than whole-recording summaries. These regions were used in two ways. First, matched short-time acoustic descriptors were summarised into a recording-level acoustic representation. Second, the same extracted frames were analysed through signed harmonic-offset patterns and pooled into a person-level inharmonicity representation based on covariance and geometric structure. The acoustic and inharmonicity models were evaluated separately and then combined through late probability fusion.

Among the sustained vowels, /u/ gave the strongest single-vowel benchmark. The continuous-speech acoustic model exceeded this benchmark, and the best observed performance was obtained when the acoustic and inharmonicity scores were combined using simple weighted fusion. The inharmonicity model was weaker as a standalone classifier, but the fusion results suggest that it may provide complementary information to the acoustic representation.

These results support the use of continuous speech for Parkinson's disease (PD) detection when the representation is restricted to reliable vowel-centred regions and evaluated at speaker level. The inharmonicity features did not outperform the acoustic model on their own, but the highest observed performance was obtained when their probability scores were combined with the acoustic-model scores.}},
  author       = {{Li, Rujia}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Continuous-Speech Parkinson's Disease Detection Using Acoustic and Inharmonicity Features}},
  year         = {{2026}},
}