Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech

Lee, Tan; Liu, Yuanyuan; Huang, Pei-Wen; Chien, Jen‐Tzung; Lam, Wang Kong; Yeung, Yu Ting; Law, Tsz Ting; Lee, Kathy; Kong, Anthony Pak-Hin; Law, Syk

doi:10.1109/icassp.2016.7472924

Cited by 37 publications

(22 citation statements)

References 8 publications

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“…Nevertheless, ASR free features of speech signals are considered as promising alternatives when evaluating pathological speech signals [16]. Although a lot of insightful works have been performed in the autonomous language skills evaluation domain [9,[11][12][13][23][24][25], there is still a lot of room left for research. Hence, herein we incorporated ASR free features of pathological speech characteristics to develop a quantitative and objective aphasia assessment tool, which differentially diagnoses Anomic and Wernicke's aphasia.…”

Section: Discussionmentioning

confidence: 99%

“…In general, a majority of the standardized aphasia assessments follow a manual procedure conducted by a speech and language pathologist (SLP). Conventional aphasia assessments take a considerable amount of time and require expert knowledge on linguistic and cultural background [11]. In conventional assessments, SLPs do not evaluate skill levels quantitatively, and thus, they become statistically unreliable.…”

Section: Introductionmentioning

confidence: 99%

“…The practicability of the test was confirmed through measured magnitude of speech errors among patients with left hemisphere disorders (LHD) [9]. Fraser et al used ASR to generate transcripts of spoken narratives of healthy controls and patients with semantic dementia and non-fluent progressive aphasia [11]. Although this work assures the feasibility of an ASR-assisted assessment process in diagnosing aphasia, interactions between features, i.e., text and acoustic features, were considered less.…”

Section: Introductionmentioning

confidence: 99%

“…It automates the quantitative analysis of spontaneous speech and language to assess a wide range of neuropsychological impairments including aphasia, dementia, schizophrenia, autism, and epilepsy [13]. However, most of the proposed software solutions are bound to one language, which hinders generalizability in multi-lingual communities [11,14,15]. Thus, there is a need for a generalizable automated software solution competent of making efficient and accurate diagnosis decisions.…”

Section: Introductionmentioning

confidence: 99%

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Development of Computer-Aided Semi-Automatic Diagnosis System for Chronic Post-Stroke Aphasia Classification with Temporal and Parietal Lesions: A Pilot Study

et al. 2020

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Survivors of either a hemorrhagic or ischemic stroke tend to acquire aphasia and experience spontaneous recovery during the first six months. Nevertheless, a considerable number of patients sustain aphasia and require speech and language therapy to overcome the difficulties. As a preliminary study, this article aims to distinguish aphasia caused from a temporoparietal lesion. Typically, temporal and parietal lesions cause Wernicke’s aphasia and Anomic aphasia. Differential diagnosis between Anomic and Wernicke’s has become controversial and subjective due to the close resemblance of Wernicke’s to Anomic aphasia when recovering. Hence, this article proposes a clinical diagnosis system that incorporates normal coupling between the acoustic frequencies of speech signals and the language ability of temporoparietal aphasias to delineate classification boundary lines. The proposed inspection system is a hybrid scheme consisting of automated components, such as confrontation naming, repetition, and a manual component, such as comprehension. The study was conducted involving 30 participants clinically diagnosed with temporoparietal aphasias after a stroke and 30 participants who had experienced a stroke without aphasia. The plausibility of accurate classification of Wernicke’s and Anomic aphasia was confirmed using the distinctive acoustic frequency profiles of selected controls. Accuracy of the proposed system and algorithm was confirmed by comparing the obtained diagnosis with the conventional manual diagnosis. Though this preliminary work distinguishes between Anomic and Wernicke’s aphasia, we can claim that the developed algorithm-based inspection model could be a worthwhile solution towards objective classification of other aphasia types.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of Computer-Aided Semi-Automatic Diagnosis System for Chronic Post-Stroke Aphasia Classification with Temporal and Parietal Lesions: A Pilot Study

et al. 2020

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“…Past ASR experiments on dysarthric speech mostly included GMM-HMM systems [15][16][17][18][19][20]. More recently Lee et al [21] reported ASR performance on Cantonese aphasic speech and disordered voice. A generic DNN-HMM system provided significant improvements on disordered voice and minor improvements on aphasic speech compared to a GMM-HMM system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage DNN Training for Automatic Recognition of Dysarthric Speech

Yılmaz¹,

Ganzeboom²,

Cucchiarini³

et al. 2017

Interspeech 2017

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Incorporating automatic speech recognition (ASR) in individualized speech training applications is becoming more viable thanks to the improved generalization capabilities of neural network-based acoustic models. The main problem in developing applications for dysarthric speech is the relative in-domain data scarcity. Collecting representative amounts of dysarthric speech data is difficult due to rigorous ethical and medical permission requirements, problems in accessing patients who are generally vulnerable and often subject to altering health conditions and, last but not least, the high variability in speech resulting from different pathological conditions. Developing such applications is even more challenging for languages which in general have fewer resources, fewer speakers and, consequently, also fewer patients than English, as in the case of a mid-sized language like Dutch. In this paper, we investigate a multi-stage deep neural network (DNN) training scheme aimed at obtaining better modeling of dysarthric speech by using only a small amount of in-domain training data. The results show that the system employing the proposed training scheme considerably improves the recognition of Dutch dysarthric speech compared to a baseline system with single-stage training only on a large amount of normal speech or a small amount of in-domain data.

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