A comparative study of classification techniques for English to Arabic speech recognition

العناوين الأخرى

دراسة مقارنة لتقنيات التصنيف للتعرف على ترجمة الكلام من اللغة الإنجليزية إلى اللغة العربية

مقدم أطروحة جامعية

al-Umari, Ayyub Abd al-Rahman

مشرف أطروحة جامعية

Nasir al-Din, Hibah Hasan O.

أعضاء اللجنة

al-Husayni, Muhammad Abbas Fadil
Tani, Ahmad

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم نظم المعلومات الحاسوبية

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2016

الملخص الإنجليزي

Speech processing is considered to be one of the most important application area of digital signal processing.

Speech recognition and translation systems have consisted into two main systems, the first system represents an ASR system that contains two levels which are level one the feature extraction level As well as, level two the classification technique level using Data Time Wrapping (DTW), Hidden Markov Model (HMM), and Dynamic Bayesian Network (DBN).

The second system is the Machine Translation (MT) system that mainly can be achieved by using three approaches which are (A) the statistical-based approach, (B) rule -approach, and (C) hybrid-based approach.

In this study, we made a comparative study between classification techniques from ASR point of view, as well as, the translation approaches from MT point of view.

The recognition rate was used in the ASR level and the error rate was used to evaluate the accuracy of the translated sentences.

Furthermore, we classified the sample text audio files into four categories which were news, conversational, scientific phrases, and control categories.

The empirical findings showed that the DBN achieved the best recognition rate for news category with 79.2% compared with HMM and DTW.

However, the HMM classification technique achieved the highest accuracy in term of recognition rate for conversational with 80.1%, scientific phrases with 86%, and control with 63.8 % recognition rates.

In contrast, using DTW in ASR had a negative behavior on the recognition rate for all speech categories.

The rule-based model which was represented by IBM Watson cloud achieved high translation accuracy results for the majority of speech categories with 13.93% in conversational, 7.38% in scientific phrases, and 17.91% in control categories.

However, by using the statistical-based model – that was represented by Google Translate - in translation the empirical findings showed that for conversational and scientific phrases the error rate was close to rule based with an intangible difference.

In contrast, by using the hybrid-based model influenced the error rate in the three ASR classification techniques and for all speech categories which was assigned as a negative effects.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

99

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-721130