You speak, we detect: Quantitative diagnosis of anomic and Wernicke's aphasia using digital signal processing techniques

摘要

Aphasia is a common adult language disorder acquired after a stroke, head injury, tumor, etc. Accurate diagnosis influences the prognosis of any speech and language disorder including aphasia. Therefore, in this paper we have proposed a semi-automated Aphasia diagnosis and classification framework employing feature extraction and pattern matching techniques of the digital signal processing (DSP). The proposed scheme evaluates the acoustic properties, time consumed, and speech characteristics for each language component i.e. naming, repetition, and comprehension. The naming and repetition tasks utilize DSP techniques. The proposed solution is highly scalable since it determines the diagnosis based on acoustic properties instead of the language characteristics. Thus, it eases extending into multiple languages. The mathematical relationships calculate the corresponding score for each component. The framework then determines the diagnosis according to the obtained scores. Since it occupies computational analysis of the speech signals, it reduces the subjectivity of the manual diagnosis process, meanwhile increasing the efficiency and accuracy by consistent diagnosis decisions. Finally, it distinguishes two sub types of Aphasia i.e. Anomic Aphasia and Wernicke's Aphasia. The results clearly revealed the efficiency improvement achieved by replacing the live auditory model with pre-recorded auditory model.