Acoustic and perceptual speech characteristics of native Mandarin speakers with Parkinson's disease.

This study examines acoustic features of speech production in speakers of Mandarin with Parkinson's disease (PD) and relates them to intelligibility outcomes. Data from 11 participants with PD and 7 controls are compared on several acoustic measures. In speakers with PD, the strength of association between these measures and intelligibility is investigated. Speakers with PD exhibited significant differences in fundamental frequency, pitch variation, vowel space, and rate relative to controls. However, in contrast to the English studies, speech rate was consistently slow and most strongly correlated with intelligibility. Thus, acoustic cues that strongly influence intelligibility in PD may vary cross-linguistically.

The Effects of Intensive Voice Treatment in Mandarin Speakers With Parkinson's Disease: Acoustic and Perceptual Findings.

PURPOSE: This study investigated the effects of intensive voice treatment on subjective and objective measures of speech production in Mandarin speakers with hypokinetic dysarthria. METHOD: Nine Mandarin speakers with hypokinetic dysarthria due to Parkinson's disease received 4 weeks of intensive voice treatment (4 × 60 min per week). The speakers were recorded reading a passage before treatment (PRE), immediately after treatment (POST), and at 6-month follow-up (FU). Listeners (n = 15) rated relative ease of understanding (EOU) of paired speech samples on a visual analogue scale. Acoustic analyses were performed. Changes in EOU, vocal intensity, global and local fundamental frequency (f o) variation, speech rate, and acoustic vowel space area (VSA) were examined. RESULTS: Increases were found in EOU and vocal intensity from PRE to POST and from PRE to FU, with no change found from POST to FU. Speech rate increased from PRE to POST, with limited evidence of an increase from PRE to FU and no change from POST to FU. No changes in global or local f o variation or in VSA were found. CONCLUSIONS: Intensive voice treatment shows promise for improving speech production in Mandarin speakers with hypokinetic dysarthria. Vocal intensity, speech rate, and, crucially, intelligibility, may improve for up to 6 months posttreatment. In contrast, f o variation and VSA may not increase following the treatment. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19529017.

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features.

Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g., training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.

Objective Intelligibility Assessment by Automated Segmental and Suprasegmental Listening Error Analysis.

Purpose Subjective speech intelligibility assessment is often preferred over more objective approaches that rely on transcript scoring. This is, in part, because of the intensive manual labor associated with extracting objective metrics from transcribed speech. In this study, we propose an automated approach for scoring transcripts that provides a holistic and objective representation of intelligibility degradation stemming from both segmental and suprasegmental contributions, and that corresponds with human perception. Method Phrases produced by 73 speakers with dysarthria were orthographically transcribed by 819 listeners via Mechanical Turk, resulting in 63,840 phrase transcriptions. A protocol was developed to filter the transcripts, which were then automatically analyzed using novel algorithms developed for measuring phoneme and lexical segmentation errors. The results were compared with manual labels on a randomly selected sample set of 40 transcribed phrases to assess validity. A linear regression analysis was conducted to examine how well the automated metrics predict a perceptual rating of severity and word accuracy. Results On the sample set, the automated metrics achieved 0.90 correlation coefficients with manual labels on measuring phoneme errors, and 100% accuracy on identifying and coding lexical segmentation errors. Linear regression models found that the estimated metrics could predict a significant portion of the variance in perceptual severity and word accuracy. Conclusions The results show the promising development of an objective speech intelligibility assessment that identifies intelligibility degradation on multiple levels of analysis.

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm.

MOTIVATION: Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. CONCLUSION: On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.

Convex weighting criteria for speaking rate estimation.

Speaking rate estimation directly from the speech waveform is a long-standing problem in speech signal processing. In this paper, we pose the speaking rate estimation problem as that of estimating a temporal density function whose integral over a given interval yields the speaking rate within that interval. In contrast to many existing methods, we avoid the more difficult task of detecting individual phonemes within the speech signal and we avoid heuristics such as thresholding the temporal envelope to estimate the number of vowels. Rather, the proposed method aims to learn an optimal weighting function that can be directly applied to time-frequency features in a speech signal to yield a temporal density function. We propose two convex cost functions for learning the weighting functions and an adaptation strategy to customize the approach to a particular speaker using minimal training. The algorithms are evaluated on the TIMIT corpus, on a dysarthric speech corpus, and on the ICSI Switchboard spontaneous speech corpus. Results show that the proposed methods outperform three competing methods on both healthy and dysarthric speech. In addition, for spontaneous speech rate estimation, the result show a high correlation between the estimated speaking rate and ground truth values.