운율 정보를 이용한 마비말장애 음성 자동 검출 및 평가

학위논문 (석사) -- 서울대학교 대학원 : 인문대학 언어학과, 2020. 8. Minhwa Chung.말장애는 신경계 또는 퇴행성 질환에서 가장 빨리 나타나는 증 상 중 하나이다. 마비말장애는 파킨슨병, 뇌성 마비, 근위축성 측삭 경화증, 다발성 경화증 환자 등 다양한 환자군에서 나타난다. 마비말장애는 조음기관 신경의 손상으로 부정확한 조음을 주요 특징으로 가지고, 운율에도 영향을 미치는 것으로 보고된다. 선행 연구에서는 운율 기반 측정치를 비장애 발화와 마비말장애 발화를 구별하는 것에 사용했다. 임상 현장에서는 마비말장애에 대한 운율 기반 분석이 마비말장애를 진단하거나 장애 양상에 따른 알맞은 치료법을 준비하는 것에 도움이 될 것이다. 따라서 마비말장애가 운율에 영향을 미치는 양상뿐만 아니라 마비말장애의 운율 특징을 긴밀하게 살펴보는 것이 필요하다. 구체 적으로, 운율이 어떤 측면에서 마비말장애에 영향을 받는지, 그리고 운율 애가 장애 정도에 따라 어떻게 다르게 나타나는지에 대한 분석이 필요하다. 본 논문은 음높이, 음질, 말속도, 리듬 등 운율을 다양한 측면에 서 살펴보고, 마비말장애 검출 및 평가에 사용하였다. 추출된 운율 특징들은 몇 가지 특징 선택 알고리즘을 통해 최적화되어 머신러닝 기반 분류기의 입력값으로 사용되었다. 분류기의 성능은 정확도, 정밀도, 재현율, F1-점수로 평가되었다. 또한, 본 논문은 장애 중증도(경도, 중등도, 심도)에 따라 운율 정보 사용의 유용성을 분석하였다. 마지막으로, 장애 발화 수집이 어려운 만큼, 본 연구는 교차 언어 분류기를 사용하였다. 한국어와 영어 장애 발화가 훈련 셋으로 사용되었으며, 테스트셋으로는 각 목표 언어만이 사용되었다. 실험 결과는 다음과 같이 세 가지를 시사한다. 첫째, 운율 정보 를 사용하는 것은 마비말장애 검출 및 평가에 도움이 된다. MFCC 만을 사용했을 때와 비교했을 때, 운율 정보를 함께 사용하는 것이 한국어와 영어 데이터셋 모두에서 도움이 되었다. 둘째, 운율 정보는 평가에 특히 유용하다. 영어의 경우 검출과 평가에서 각각 1.82%와 20.6%의 상대적 정확도 향상을 보였다. 한국어의 경우 검출에서는 향상을 보이지 않았지만, 평가에서는 13.6%의 상대적 향상이 나타났다. 셋째, 교차 언어 분류기는 단일 언어 분류기보다 향상된 결과를 보인다. 실험 결과 교차언어 분류기는 단일 언어 분류기와 비교했을 때 상대적으로 4.12% 높은 정확도를 보였다. 이것은 특정 운율 장애는 범언어적 특징을 가지며, 다른 언어 데이터를 포함시켜 데이터가 부족한 훈련 셋을 보완할 수 있 음을 시사한다.One of the earliest cues for neurological or degenerative disorders are speech impairments. Individuals with Parkinsons Disease, Cerebral Palsy, Amyotrophic lateral Sclerosis, Multiple Sclerosis among others are often diagnosed with dysarthria. Dysarthria is a group of speech disorders mainly affecting the articulatory muscles which eventually leads to severe misarticulation. However, impairments in the suprasegmental domain are also present and previous studies have shown that the prosodic patterns of speakers with dysarthria differ from the prosody of healthy speakers. In a clinical setting, a prosodic-based analysis of dysarthric speech can be helpful for diagnosing the presence of dysarthria. Therefore, there is a need to not only determine how the prosody of speech is affected by dysarthria, but also what aspects of prosody are more affected and how prosodic impairments change by the severity of dysarthria. In the current study, several prosodic features related to pitch, voice quality, rhythm and speech rate are used as features for detecting dysarthria in a given speech signal. A variety of feature selection methods are utilized to determine which set of features are optimal for accurate detection. After selecting an optimal set of prosodic features we use them as input to machine learning-based classifiers and assess the performance using the evaluation metrics: accuracy, precision, recall and F1-score. Furthermore, we examine the usefulness of prosodic measures for assessing different levels of severity (e.g. mild, moderate, severe). Finally, as collecting impaired speech data can be difficult, we also implement cross-language classifiers where both Korean and English data are used for training but only one language used for testing. Results suggest that in comparison to solely using Mel-frequency cepstral coefficients, including prosodic measurements can improve the accuracy of classifiers for both Korean and English datasets. In particular, large improvements were seen when assessing different severity levels. For English a relative accuracy improvement of 1.82% for detection and 20.6% for assessment was seen. The Korean dataset saw no improvements for detection but a relative improvement of 13.6% for assessment. The results from cross-language experiments showed a relative improvement of up to 4.12% in comparison to only using a single language during training. It was found that certain prosodic impairments such as pitch and duration may be language independent. Therefore, when training sets of individual languages are limited, they may be supplemented by including data from other languages.1. Introduction 1 1.1. Dysarthria 1 1.2. Impaired Speech Detection 3 1.3. Research Goals & Outline 6 2. Background Research 8 2.1. Prosodic Impairments 8 2.1.1. English 8 2.1.2. Korean 10 2.2. Machine Learning Approaches 12 3. Database 18 3.1. English-TORGO 20 3.2. Korean-QoLT 21 4. Methods 23 4.1. Prosodic Features 23 4.1.1. Pitch 23 4.1.2. Voice Quality 26 4.1.3. Speech Rate 29 4.1.3. Rhythm 30 4.2. Feature Selection 34 4.3. Classification Models 38 4.3.1. Random Forest 38 4.3.1. Support Vector Machine 40 4.3.1 Feed-Forward Neural Network 42 4.4. Mel-Frequency Cepstral Coefficients 43 5. Experiment 46 5.1. Model Parameters 47 5.2. Training Procedure 48 5.2.1. Dysarthria Detection 48 5.2.2. Severity Assessment 50 5.2.3. Cross-Language 51 6. Results 52 6.1. TORGO 52 6.1.1. Dysarthria Detection 52 6.1.2. Severity Assessment 56 6.2. QoLT 57 6.2.1. Dysarthria Detection 57 6.2.2. Severity Assessment 58 6.1. Cross-Language 59 7. Discussion 62 7.1. Linguistic Implications 62 7.2. Clinical Applications 65 8. Conclusion 67 References 69 Appendix 76 Abstract in Korean 79Maste

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

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

A cross-linguistic perspective to classification of healthiness of speech in Parkinson's disease

People with Parkinson's disease often experience communication problems. The current cross-linguistic study investigates how listeners' perceptual judgements of speech healthiness are related to the acoustic changes appearing in the speech of people with Parkinson's disease. Accordingly, we report on an online experiment targeting perceived healthiness of speech. We studied the relations between healthiness perceptual judgements and a set of acoustic characteristics of speech in a cross-sectional design. We recruited 169 participants, who performed a classification task judging speech recordings of Dutch speakers with Parkinson's disease and of Dutch control speakers as ‘healthy’ or ‘unhealthy’. The groups of listeners differed in their training and expertise in speech language therapy as well as in their native languages. Such group separation allowed us to investigate the acoustic correlates of speech healthiness without influence of the content of the recordings. We used a Random Forest method to predict listeners' responses. Our findings demonstrate that, independently of expertise and language background, when classifying speech as healthy or unhealthy listeners are more sensitive to speech rate, presence of phonation deficiency reflected by maximum phonation time measurement, and centralization of the vowels. The results indicate that both specifics of the expertise and language background may lead to listeners relying more on the features from either prosody or phonation domains. Our findings demonstrate that more global perceptual judgements of different listeners classifying speech of people with Parkinson's disease may be predicted with sufficient reliability from conventional acoustic features. This suggests universality of acoustic change in speech of people with Parkinson's disease. Therefore, we concluded that certain aspects of phonation and prosody serve as prominent markers of speech healthiness for listeners independent of their first language or expertise. Our findings have outcomes for the clinical practice and real-life implications for subjective perception of speech of people with Parkinson's disease, while information about particular acoustic changes that trigger listeners to classify speech as ‘unhealthy’ can provide specific therapeutic targets in addition to the existing dysarthria treatment in people with Parkinson's disease

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

Rhythmic performance in hypokinetic dysarthria : relationship between reading, spontaneous speech and diadochokinetic tasks

Purpose: This study aimed to investigate whether rhythm metrics are sensitive to change in speakers with mild hypokinetic dysarthria, whether such changes can be detected in reading and spontaneous speech, and whether diadochokinetic (DDK) performance relates to rhythmic properties of speech tasks. Method: Ten people with Parkinson’s Disease (PwPD) with mild hypokinetic dysarthria and ten healthy control speakers produced DDK repetitions, a reading passage and a spontaneous monologue. Articulation rate, as well as ten rhythm metrics were applied to the speech data. DDK performance was captured by mean, standard deviation (SD) and coefficient of variation (CoV) of syllable duration. Results: Group differences were apparent across both speech tasks, but mainly in spontaneous speech. The control speakers changed their rhythm performance between the two tasks, whereas the PwPD displayed a more constant behaviour. The correlation analysis of speech and DDK tasks resulted in few meaningful relationships. Conclusions: Rhythm metrics appeared to be sensitive to mild levels of impairment in PwPD. They are thus suitable for use as diagnostic or outcome measures. In addition, we demonstrated that conversational data can be used in the investigation of rhythm. Finally, the value of DDK tasks in predicting the rhythm performance during speech could not be demonstrated successfully

More than words: Recognizing speech of people with Parkinson's disease

Parkinson’s disease (PD) is the fastest-growing neurological disorder in the world, with approximately 10 million people currently living with the diagnosis. Hypokinetic dysarthria (HD) is one of the symptoms that appear in early stages of the disease progression. The main aim of this dissertation is to gain insights into listeners’ impressions of dysarthric speech and to uncover acoustic correlates of those impressions. We do this by exploring two sides of communication: speech production of people with PD, and listeners’ recognition of speech of people with PD. Therefore, the studies in this dissertation approach the topic of speech changes in PD from both the speakers' side - via acoustic analysis of speech, and the listeners' side - via experiments exploring the influence of expertise and language background on recognition of speech of people with PD. Moreover, to obtain a more comprehensive picture of these perspectives, the studies of this dissertation are multifaceted, explore cross-linguistic aspects of dysarthric speech recognition and include both cross-sectional and longitudinal designs. The results demonstrate that listeners' ability to recognize speech of people with PD as unhealthy is rooted in the acoustic changes in speech, not in its content. Listeners’ experience in the fields of speech and language therapy or speech sciences affect dysarthric speech recognition. The results also suggest that tracking speech parameters is a useful tool for monitoring the progression and/or development of dysarthria and objectively evaluating long-term effects of speech therapy