Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Abstract

Dysarthria is a common symptom for people with Parkinson's disease (PD), which affects respiration, phonation, articulation and prosody, and reduces the speech intelligibility as a result. Imprecise vowel articulation can be observed in people with PD. Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its detection and assessment. Standard clinical vowel articulation features include the first two formants of the three corner vowels /a/, /i/ and /u/, from which clinically relevant parameters such as vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR) are derived. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming and requires specific expertise in speech signal analysis. 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 method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/) scores in a fully automated manner. 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. Results from these two corpora further demonstrate the efficacy of the automated features in separating PD/dysarthric speakers from controls, and that the features are correlated with Parkinson's disease severity ratings on PC-GITA and with level of dysarthria on TORGO.

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

“…In Some Cases, Studies Incorporate More Than One Technique. Following That, the Same Study Was Replicated, Thereby Increasing the Overall Number of Research Studies Method/Technique % Reference MFCCs/derived features from MFCCs 35.4% [ 13 , 15 , 55 , 70 , 72 , 80 , 87 , 94 , 95 , 97–99 , 101 , 112 , 117 , 127 , 131 , 133 , 134 ] Spectro-Temporal of utterances/keywords 26.15% [ 12 , 14 , 15 , 26 , 63 , 72 , 87 , 89 , 90 , 97 , 101–103 , 110 , 112 , 124 , 136 ] Articulation way/Speech timing 18.5% [ 13 , 27 , 58 , 81 , 86–88 , 111 , 113 , 124 , 129 ] Glottal flow 7.7% [ 58 , …”

“…In the context of speech disorder, the patient can use an ASR to detect voice disorder and the voice pathologist to make an intelligent assessment of the patient. 55 The performance of the ASR mainly depends on the training dataset, which is sorted into training and testing sets by randomly selecting the observations from healthy and sick voices. 56 The learning set is used to build the machine learning model, while the testing set is used to evaluate the final model's performance and generalization.…”

“…In the context of speech disorder, the patient can use an ASR to detect voice disorder and the voice pathologist to make an intelligent assessment of the patient. 55

Figure 3 ASR Architecture.

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“…Although the researchers found several ways and techniques for better detecting Dysarthria, analysis indicates that the combination of MFCC and Spectro Temporal utterance methods, such as, 87,94,124,138 achieved better accuracy than others. [13,15,55,70,72,80,87,94,95,[97][98][99]101,112,117,127,131,133,134] Spectro-Temporal of utterances/keywords 26.15% [12,14,15,26,63,72,87,89,90,97,[101][102][103]110,112,124,136] Articulation way/Speech timing 18.5% [13,27,58,81,[86][87][88]111,…”

Automatic Assessment of Parkinson's Disease Using Speech Representations of Phonation and Articulation

“…A subset of a Finnish PD speech corpus, PDSTU [51], was used for the PD assessment experiments in this work. PDSTU speech has been recorded in the mono channel with 32 bits and a sampling rate of 44.1 kHz with a close-talking microphone.…”