Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches

• 作者: Lahouari Cheded Omair Khan Sunday Olusanya Olatunji Wasfi G. Al-Khatib
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 医学
• 关键词: ARABIC Monologues Prosodic Features Type-2 FUZZY LOGIC Systems Sensitivity Based LINEAR LearningMethod Support Vector Machines
• 年，卷（期）: 2013,（3）
• 页码范围: 165-175
• 页数: 11页
• 分类号: R73

摘要

In this paper, we extend our previous study of addressing the important problem of automatically identifying question and non-question segments in Arabic monologues using prosodic features. We propose here two novel classification approaches to this problem: one based on the use of the powerful type-2 fuzzy logic systems (type-2 FLS) and the other on the use of the discriminative sensitivity-based linear learning method (SBLLM). The use of prosodic features has been used in a plethora of practical applications, including speech-related applications, such as speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. In this paper, we continue to specifically focus on the Arabic language, as other languages have received a lot of attention in this regard. Moreover, we aim to improve the performance of our previously-used techniques, of which the support vector machine (SVM) method was the best performing, by applying the two above-mentioned powerful classification approaches. The recorded continuous speech is first segmented into sentences using both energy and time duration parameters. The prosodic features are then extracted from each sentence and fed into each of the two proposed classifiers so as to classify each sentence as a Question or a Non-Question sentence. Our extensive simulation work, based on a moderately-sized database, showed the two proposed classifiers outperform SVM in all of the experiments carried out, with the type-2 FLS classifier consistently exhibiting the best performance, because of its ability to handle all forms of uncertainties.