SPECTRAL/CEPSTRAL ANALYSIS OF VOICE QUALITY IN PATIENTS WITH PARKINSONS DISEASE

The purpose of this dissertation was to determine whether Silverman Voice Treatment (LSVT) affects cepstral/spectral measures of voice quality in speakers with idiopathic Parkinsons Disease (PD). The first study investigated the effect of LSVT on cepstral/spectral measures of sustained // vowels to determine whether voice quality improves. Few studies have investigated the effects of LSVT on voice quality using acoustic measures, and none have used cepstral measures. The first study investigated the effect of LSVT on cepstral/spectral analyses of sustained // vowels produced by speakers. Sustained vowels were analyzed for cepstral peak prominence (CPP), CPP Standard Deviation (CPP-SD), Low/High Spectral Ratio (L/H SR), and Cepstral/Spectral Index of Dysphonia (CSID) using the Analysis of Dysphonia in Speech and Voice (ADSV) program. The study found both improved harmonic structure and voice quality as reflected in cepstral/spectral measures. Voice quality in connected speech is important because it is representative of how a typical individual communicates. Thus, the second studys goals were: First, to investigate the effect of LSVT on cepstral/spectral analysis of connected speech; and second, to compare cepstral/spectral analyses findings in connected speech with findings observed in sustained phonation. Another goal was to examine individual differences in response to treatment and compare them to individual changes observed in sustained phonation. The results demonstrated that CPP increased significantly following LSVT, indicating improved harmonic dominance as a result of treatment, and CSID decreased following LSVT, indicating a reduction of the overall severity in connected speech at the group level. Analysis of individual differences demonstrated that only four participants improved by at least one half Standard Deviation (SD) following treatment in CPP, CPP-SD, and CSID in both sustained phonation and connected speech tasks. Three showed a reduction in L/H SR in sustained phonation and only one showed an increase in L/H SR in connected speech. The other participants improvement varied, but the majority demonstrated voice quality improvement in sustained phonation. The overall results indicated that CPP and CSID were strong acoustic measures for demonstrating voice quality improvement following treatment in both tasks connected speech and sustained phonation

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Role of Therapeutic Devices in Enhancing Speech Intelligibility and Vocal Intensity in an Individual with Parkinson’s Disease

The prevailing speech therapy techniques for treating hypokinetic dysarthria in individuals with Parkinson\u27s disease (PD) yields improvements within the clinical setting, however, maintenance and generalization of acquired behaviors continue to be a challenge. The purpose of this study was to investigate the effects of portable therapeutic devices including Ambulatory Phonation Monitor with biofeedback (APM) and auditory masker in maintenance and carryover of improved speech. Our participant was an individual diagnosed with PD for the past 25 years who continued to display speech disturbances despite undergoing several behavioral speech therapy programs and neurosurgical procedures. Speech intelligibility and average intensity measures under automatic, elicited, and spontaneous speech tasks were recorded pre- and postusage of APM and auditory masker for a period of 1 week each. Preliminary findings showed no significant difference in the measures between means (P\u3e0.05) across all tasks for both the devices. Suggestions for future research on therapeutic devices are discussed

Diagnosis of Parkinson’s Disease using Principal Component Analysis and Boosting Committee Machines

Parkinson’s disease (PD) has become one of the most common degenerative disorders of the central nervous system. In this study, our main goal was to discriminate between healthy people and people with Parkinson’s disease. In order to achieve this we used artificial neural networks, and dataset taken from University of California, Irvine machine learning database, having 48 normal and 147 PD cases. We examine the performance of neural network systems with back propagation together with a majority voting scheme. In order to train examples we used boosting by filtering technique with seven committee machines, and principal component analysis is used for data reduction. The experimental results have demonstrated that the combination of these proposed methods has obtained very good results with correct positive value of 92% on the classification of PD.

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Group Speech Therapy in Individuals With Parkinson Disease: Face-to-Face Versus Telemedicine

The purpose of this study was to evaluate the outcomes of group speech therapy for individuals with Parkinson Disease (IWPD) in general and to compare outcomes of group treatment delivered face-to-face (FtF) versus delivery via telemedicine (TM). Twenty-seven IWPD received group treatment based on a modified version of LSVT® in either an FtF or TM format. Outcome measures were collected pre- and post-treatment, which included vocal intensity (dB), Voice Handicap Index (VHI) scores, and self-ratings. Results indicated that vocal intensity and self-ratings of loudness significantly increased for both the FtF and TM groups. VHI scores and the five remaining self-ratings were not significantly improved for either group following treatment, although the data on all measures from the FtF group did show improvement. The findings of this study support the short-term effectiveness of FtF and TM group therapy for improving vocal intensity and participant self-ratings of loudness in IWPD

Speech function in persons with Parkinson\u27s disease: effects of environment, task and treatment

Parkinson’s Disease (PD) is a degenerative neurological disease affecting aspects of movement, including speech. Persons with PD are reported to have better speech functioning in the clinical setting than in the home setting, but this has not been quantified. New methodologies in ambulatory measures of speech are emerging that allow investigation of non-clinical settings. The following questions are addressed: Is speech different between environments in PD and in healthy controls? Can clinical tasks predict speech behaviors in the home? Is treatment proven effective by measures in the home? What can we glean from methods of measurement of speech function in the home? The experiment included 13 persons with PD and 12 healthy controls, studied in the clinical and home environments, and 7 of those 13 persons with PD participated in a treatment study. Major findings included: Spontaneous speech intelligibility, not intensity, was the differentiating factor between persons with PD and healthy controls. Intelligibility and intensity were not related. Both groups presented with higher sentence intensity in the home environment. Spontaneous speech intelligibility in the clinic was related to spontaneous speech intelligibility in the home. The Sentence Intelligibility Test emerged as the best predictor of spontaneous speech intelligibility in the home. Differences between pilot treatment groups measured in the home on intensity and intelligibility were not large enough to make a clinical trial feasible. Individual differences may account for many of these results, for example more severely impaired patients may have shown different data. Drawing conclusions regarding the home environment via measures outside the home should be carefully considered. Ambulatory measures of speech are a viable option for studying speech function in non-clinical settings, and technology is advancing. Further investigation is needed to develop methodologies and normative values for speech in the home

Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition

Parkinson’s disease (PD) is a global public health problem of enormous dimension. In this study, we aimed to discriminate between healthy people and people with Parkinson’s disease (PD). Various studies revealed, that voice is one of the earliest indicator of PD, and for that reason, Parkinson dataset that contains biomedical voice of human is used. The main goal of this paper is to automatically detect whether the speech/voice of a person is affected by PD. We examined the performance of fuzzy c-means (FCM) clustering and pattern recognition methods on Parkinson’s disease dataset. The first method has the main aim to distinguish performance between two classes, when trying to differentiate between normal speaking persons and speakers with PD. This method could greatly be improved by classifying data first and then testing new data using these two patterns. Thus, second method used here is pattern recognition. The experimental results have demonstrated that the combination of the fuzzy c-means method and pattern recognition obtained promising results for the classification of PD