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

Joint Authors

Khalifah, Uthman
Abu Shariah, Muhammad
Zayn al-Din, Roziati
Ainon, Raja Nur

Source

The International Arab Journal of Information Technology

Issue

Vol. 9, Issue 1 (31 Jan. 2012), pp.84-93, 10 p.

Publisher

Zarqa University

Publication Date

2012-01-31

Country of Publication

Jordan

No. of Pages

10

Main Subjects

Information Technology and Computer Science

Topics

Abstract EN

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 seconds.

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.

American Psychological Association (APA)

Abu Shariah, Muhammad& Ainon, Raja Nur& Zayn al-Din, Roziati& Khalifah, Uthman. 2012. Arabic speaker-independent continuous automatic speech recognition based on a phonetically rich and balanced speech corpus. The International Arab Journal of Information Technology،Vol. 9, no. 1, pp.84-93.
https://search.emarefa.net/detail/BIM-274315

Modern Language Association (MLA)

Abu Shariah, Muhammad…[et al.]. Arabic speaker-independent continuous automatic speech recognition based on a phonetically rich and balanced speech corpus. The International Arab Journal of Information Technology Vol. 9, no. 1 (Jan. 2012), pp.84-93.
https://search.emarefa.net/detail/BIM-274315

American Medical Association (AMA)

Abu Shariah, Muhammad& Ainon, Raja Nur& Zayn al-Din, Roziati& Khalifah, Uthman. Arabic speaker-independent continuous automatic speech recognition based on a phonetically rich and balanced speech corpus. The International Arab Journal of Information Technology. 2012. Vol. 9, no. 1, pp.84-93.
https://search.emarefa.net/detail/BIM-274315

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 91-92

Record ID

BIM-274315