A machine learning based system for the automatic evaluation of aphasia speech

Kohlschein, Christian; Schmitt, Maximilian; Schuller, Björn; Jeschke, Sabina; Werner, Cornelius J.

doi:10.1109/healthcom.2017.8210766

Cited by 22 publications

(14 citation statements)

References 21 publications

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“…Further work should revisit these questions with recordings that are not centered on children. Indeed, a growing field of research is investigating the possibility of using adults' speech as a potential biomarker (e.g., [28]). We believe that adult-centered recordings will be, on average, less challenging than child-centered ones, with difficulty levels increasing for certain neurological conditions affecting speech production (e.g., aphasia).…”

Section: Discussionmentioning

confidence: 99%

Talker Diarization in the Wild: the Case of Child-centered Daylong Audio-recordings

Cristià¹,

Ganesh²,

Casillas³

et al. 2018

Interspeech 2018

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Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers-the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild.

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Section: Discussionmentioning

confidence: 99%

Talker Diarization in the Wild: the Case of Child-centered Daylong Audio-recordings

Cristià¹,

Ganesh²,

Casillas³

et al. 2018

Interspeech 2018

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“…Also, they suggested that the selection of the classifier is task-dependent. In [ 13 ], Kohlschein et al proposed an ML-based multi-class automatic aphasia assessment system to classify various aphasia syndromes. Their classification model achieved a low accuracy of 44.3%, and they suggested that a large dataset was required to improve the classification performance.…”

Section: Related Workmentioning

confidence: 99%

“…Speech assessment is used extensively in the diagnosis of Parkinson’s and aphasia diseases [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Aphasia is an acquired neurogenic language disorder that can be evaluated with one of the well-known assessment tools, such as the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE [ 15 ], for Chinese-dialect-speaking patients), the Aachen Aphasia Test (AAT [ 16 ], for German-speaking patients) and the Boston Diagnostic Aphasia Examination (BDAE [ 17 ], for English-speaking patients).…”

Section: Introductionmentioning

confidence: 99%

“…Commonly, there are three aphasia assessment tasks whereby an SLP performs a comprehensive examination of the patient’s communication abilities, including speaking, expressing ideas, understanding language, reading, and writing. These tasks are the discrimination between normal and aphasic speech [ 9 ], the assessment of the degree of severity of impairment for aphasic patients [ 10 , 12 ], and the classification of aphasia syndromes (such as Global aphasia, Broca’s aphasia, Wernicke’s aphasia and amnesic aphasia) [ 13 , 14 ]. Conventional methods of aphasia assessment and rehabilitation are resource-intensive processes that require the presence of an SLP.…”

Section: Introductionmentioning

confidence: 99%

“…Most research on automatic aphasia assessment [ 5 , 9 , 10 , 12 , 13 , 14 , 18 ] has focused on a single aphasia assessment task using automatic speech recognition techniques and a machine learning (ML) framework with a fixed dataset-type . Generally, three types of training datasets are used for aphasia assessment tasks: (1) healthy subjects’ dataset, (2) aphasic patients’ dataset, or (3) a combination of healthy and aphasic dataset [ 10 , 12 ] (hereafter, these datasets are referred to as dataset-type .…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Mahmoud

Kumar

et al. 2021

Sensors

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Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

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