Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks
نویسندگان
چکیده
Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks Suhas Pillai, M.S. Rochester Institute of Technology, 2017 Supervisor: Dr. Raymond Ptucha Millions of people around the world are diagnosed with neurological disorders like Parkinsons, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features.
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