Angela Roberts

Yael Segal, Kasia Hitczenko, Matthew Goldrick et al.

Diadochokinetic speech tasks (DDK), in which participants repeatedly produce syllables, are commonly used as part of the assessment of speech motor impairments. These studies rely on manual analyses that are time-intensive, subjective, and provide only a coarse-grained picture of speech. This paper presents two deep neural network models that automatically segment consonants and vowels from unannotated, untranscribed speech. Both models work on the raw waveform and use convolutional layers for feature extraction. The first model is based on an LSTM classifier followed by fully connected layers, while the second model adds more convolutional layers followed by fully connected layers. These segmentations predicted by the models are used to obtain measures of speech rate and sound duration. Results on a young healthy individuals dataset show that our LSTM model outperforms the current state-of-the-art systems and performs comparably to trained human annotators. Moreover, the LSTM model also presents comparable results to trained human annotators when evaluated on unseen older individuals with Parkinson's Disease dataset.

Amrit Romana, John Bandon, Noelle Carlozzi et al.

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorder that causes cognitive disturbances, neuropsychiatric symptoms, and impaired motor abilities (e.g., gait, speech, voice). Due to its progressive nature, HD treatment requires ongoing clinical monitoring of symptoms. Individuals with the gene mutation which causes HD may exhibit a range of speech symptoms as they progress from premanifest to manifest HD. Differentiating between premanifest and manifest HD is an important yet understudied problem, as this distinction marks the need for increased treatment. Speech-based passive monitoring has the potential to augment clinical assessments by continuously tracking manifestation symptoms. In this work we present the first demonstration of how changes in connected speech can be measured to differentiate between premanifest and manifest HD. To do so, we focus on a key speech symptom of HD: vowel distortion. We introduce a set of vowel features which we extract from connected speech. We show that our vowel features can differentiate between premanifest and manifest HD with 87% accuracy.