國立虎尾科技大學 |

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks./
作者:	Pillai, Suhas Balkrishna.
面頁冊數:	1 online resource (100 pages)
附註:	Source: Masters Abstracts International, Volume: 56-04.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369769982

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks.
Pillai, Suhas Balkrishna.

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks. - 1 online resource (100 pages)

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--Rochester Institute of Technology, 2017.

Includes bibliographical references

Millions of people around the world are diagnosed with neurological disorders like Parkinson's, 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. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369769982Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks.
LDR:02950ntm a2200337K 4500 001 915295
005 20180727125212.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369769982
035 $a (MiAaPQ)AAI10269188
035 $a (MiAaPQ)rit:12607
035 $a AAI10269188
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Pillai, Suhas Balkrishna. $3 1188610
245 1 0 $a Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks.
264 0 $c 2017
300 $a 1 online resource (100 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Advisers: Raymond Ptucha; Zack Butler.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a Millions of people around the world are diagnosed with neurological disorders like Parkinson's, 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. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Audiology. $3 889171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0300
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rochester Institute of Technology. $b Computer Science. $3 1181909
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10269188 $z click for full text (PQDT)