Speech from people with Parkinson's disease (PD) are likely to be degraded on phonation, articulation, and prosody. Motivated to describe articulation deficits comprehensively, we investigated 1) the universal phonological features that model articulation manner and place, also known as speech attributes, and 2) glottal features capturing phonation characteristics. These were further supplemented by, and compared with, prosodic features using a popular compact feature set and standard MFCC. Temporal characteristics of these features were modeled by convolutional neural networks. Besides the features, we were also interested in the speech tasks for collecting data for automatic PD speech assessment, like sustained vowels, text reading, and spontaneous monologue. For this, we utilized a recently collected Finnish PD corpus (PDSTU) as well as a Spanish database (PC-GITA). The experiments were formulated as regression problems against expert ratings of PD-related symptoms, including ratings of speech intelligibility, voice impairment, overall severity of communication disorder on PDSTU, as well as on the Unified Parkinson's Disease Rating Scale (UPDRS) on PC-GITA. The experimental results show: 1) the speech attribute features can well indicate the severity of pathologies in parkinsonian speech; 2) combining phonation features with articulatory features improves the PD assessment performance, but requires high-quality recordings to be applicable; 3) read speech leads to more accurate automatic ratings than the use of sustained vowels, but not if the amount of speech is limited to correspond to the sustained vowels in duration; and 4) jointly using data from several speech tasks can further improve the automatic PD assessment performance.

Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its assessment. Standard clinical vowel articulation features of vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR), are derived the first two formants of the three corner vowels /a/, /i/ and /u/. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming. The present work aims to reduce human effort in clinical analysis of PD speech by proposing an automatic pipeline for vowel articulation assessment. The method is based on automatic corner vowel detection using a language universal phoneme recognizer, followed by statistical analysis of the formant data. The approach removes the restrictions of prior knowledge of speaking content and the language in question. Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/). The automatically computed parameters are shown to be highly correlated with features computed with manual annotations of corner vowels. In addition, automatically and manually computed vowel articulation features have comparable correlations with experts' ratings on speech intelligibility, voice impairment and overall severity of communication disorder. Language-independence of the proposed approach is further validated on a Spanish PD database, PC-GITA, as well as on TORGO corpus of English dysarthric speech.

Traditionally acoustical assessment of voice disorder relies on simple and homogeneous speech samples like sustained vowels. Continuous speech is believed to be more representative of the daily function of voice and more preferable in clinical practice. This paper describes an attempt on automating voice assessment with continuous speech utterances. The proposed system makes use of a novel type of features that are derived from phone posterior probabilities outputted by a deep neural network based automatic speech recognition (ASR) system. These ASR-based voice features are designed to effectively quantify the mismatch between disordered voice and normal voice. Prediction of voice disorder severity is carried out first at utterance-level and subsequently the prediction scores for individual utterances from a subject are combined to give an overall-assessment on the subject. With a low-dimension ASR-based feature vector, the utterance-level prediction accuracy is comparable to that with conventional features with a much higher dimension. By jointly using the ASR features and conventional voice features, a subject-level prediction accuracy of over 80% on three severity classes can be achieved. Subjects with mild disorder and those with severe disorder could be perfectly distinguished by the proposed method.

This paper describes the application of state-of-the-art automatic speech recognition (ASR) systems to objective assessment of voice and speech disorders. Acoustical analysis of speech has long been considered a promising approach to non-invasive and objective assessment of people. In the past the types and amount of speech materials used for acoustical assessment were very limited. With the ASR technology, we are able to perform acoustical and linguistic analyses with a large amount of natural speech from impaired speakers. The present study is focused on Cantonese, which is a major Chinese dialect. Two representative disorders of speech production are investigated: dysphonia and aphasia. ASR experiments are carried out with continuous and spontaneous speech utterances from Cantonese-speaking patients. The results confirm the feasibility and potential of using natural speech for acoustical assessment of voice and speech disorders, and reveal the challenging issues in acoustic modeling and language modeling of pathological speech.