Simulating dysarthric speech for training data augmentation in clinical speech applications

Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access. As a result, clinical speech applications are typically developed using small data sets with only tens of speakers. In this paper, we propose a method for simulating training data for clinical applications by transforming healthy speech to dysarthric speech using adversarial training. We evaluate the efficacy of our approach using both objective and subjective criteria. We present the transformed samples to five experienced speech-language pathologists (SLPs) and ask them to identify the samples as healthy or dysarthric. The results reveal that the SLPs identify the transformed speech as dysarthric 65% of the time. In a pilot classification experiment, we show that by using the simulated speech samples to balance an existing dataset, the classification accuracy improves by about 10% after data augmentation.Comment: Will appear in Proc. of ICASSP 201

Articulation entropy: An unsupervised measure of articulatory precision

Articulatory precision is a critical factor that influences speaker intelligibility. In this paper, we propose a new measure we call ‘articulation entropy’ that serves as a proxy for the number of distinct phonemes a person produces when he or she speaks. The method is based on the observation that the ability of a speaker to achieve an articulatory target, and hence clearly produce distinct phonemes, is related to the variation of the distribution of speech features that capture articulation - the larger the variation, the larger the number of distinct phonemes produced. In contrast to previous work, the proposed method is completely unsupervised, does not require phonetic segmentation or formant estimation, and can be estimated directly from continuous speech. We evaluate the performance of this measure with several experiments on two data sets: a database of English speakers with various neurological disorders and a database of Mandarin speakers with Parkinson’s disease. The results reveal that our measure correlates with subjective evaluation of articulatory precision and reveals differences between healthy individuals and individuals with neurological impairment

Acoustic and perceptual speech characteristics of native Mandarin speakers with Parkinson 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

Design, synthesis, and antifungal activities of chalcone derivatives containing piperidine and sulfonamide moiety

A series of chalcone derivatives containing piperidine sulfonamide were designed and synthesized from natural chalones, and their structures were characterized on NMR and HRMS. In vitro antifungal activity test results showed that most of the target compounds exhibited good antifungal activities against a variety of fungi in vitro. X17 showed an excellent antifungal activity against S. sclerotiorum, Phomopsis sp, P. capsica with EC50 values of 10.43, 10.78, 12.02 µg/mL, respectively. It was better than those of azoxystrobin (19.34, 18.21, 63.44 µg/mL). In vivo antifungal activity test showed that X17 at the concentration of 200 µg/mL had better curative activities on rape leaves (90.8 %) than that of azoxystrobin (86.6 %). The protective activities on kiwifruits (88.3 %) were better than that of azoxystrobin (86.1 %). Mechanical studies showed that X17 inhibited the normal hypha growth by disrupting the cell membrane of S. sclerotiorum

Pharmacological activation of GPX4 ameliorates doxorubicin-induced cardiomyopathy

Due to the cardiotoxicity of doxorubicin (DOX), its clinical application is limited. Lipid peroxidation caused by excessive ferrous iron is believed to be a key molecular mechanism of DOX-induced cardiomyopathy (DIC). Dexrazoxane (DXZ), an iron chelator, is the only drug approved by the FDA for reducing DIC, but it has many side effects and cannot be used as a preventive drug in clinical practice. Single-nucleus RNA sequencing (snRNA-seq) analysis identified myocardial and epithelial cells that are susceptible to DOX-induced ferroptosis. The glutathione peroxidase 4 (GPX4) activator selenomethione (SeMet) significantly reduced polyunsaturated fatty acids (PUFAs) and oxidized lipid levels in vitro. Consistently, SeMet significantly decreased DOX-induced lipid peroxidation in H9C2 cells and mortality in C57BL/6 mice compared to DXZ, ferrostatin-1, and normal saline. SeMet can effectively reduce serum markers of cardiac injury in C57BL/6 mice and breast cancer patients. Depletion of the GPX4 gene in C57BL/6 mice resulted in an increase in polyunsaturated fatty acid (PUFA) levels and eliminated the protective effect of SeMet against DIC. Notably, SeMet exerted antitumor effects on breast cancer models with DOX while providing cardiac protection for the same animal without detectable toxicities. These findings suggest that pharmacological activation of GPX4 is a valuable and promising strategy for preventing the cardiotoxicity of doxorubicin