GDL

A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability

2010
Undergraduate Theses from gdlhub / 2017-08-14 11:52:31
Oleh : Mohammad Abushariah, Raja Ainon, Roziati Zainuddin, Moustafa Elshafei, Othman Khalifa, IAJIT
Dibuat : 2012-06-22, dengan 0 file

Keyword : Arabic automatic speech recognition, arabic speech corpus, phonetically rich and balanced, acoustic model, statistical language model
Subjek : A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.1/2745-9.pdf
Sumber pengambilan dokumen : Internet

This paper describes and proposes an efficient and effective framework for the design and development of a


speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced


speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native


speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world.


The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the


Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models


(HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the


acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state


distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers with


different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of


11.27% and 10.07% with and without diacritical marks, respectively. For different speakers with similar sentences, the system


obtained a word recognition accuracy of 95.92% and 96.29%, and a WER of 5.78%, and 5.45% with and without diacritical


marks, respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of


89.08% and 90.23%, and a WER of 15.59% and 14.44% with and without diacritical marks, respectively.

Deskripsi Alternatif :

This paper describes and proposes an efficient and effective framework for the design and development of a


speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced


speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native


speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world.


The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the


Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models


(HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the


acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state


distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers with


different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of


11.27% and 10.07% with and without diacritical marks, respectively. For different speakers with similar sentences, the system


obtained a word recognition accuracy of 95.92% and 96.29%, and a WER of 5.78%, and 5.45% with and without diacritical


marks, respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of


89.08% and 90.23%, and a WER of 15.59% and 14.44% with and without diacritical marks, respectively.

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	IAJIT
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com