利用韵律特征识别阿拉伯语独白中的问句和非问句片段:新型2型模糊逻辑和基于灵敏度的线性学习方法

S. O. Olatunji, L. Cheded, W. Al-Khatib, O. Khan
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引用次数: 3

摘要

在本文中,我们扩展了之前的研究,利用韵律特征来解决阿拉伯语独白中自动识别疑问句和非疑问句的重要问题。我们在此提出了两种新的分类方法:一种是基于使用强大的2型模糊逻辑系统(type-2 FLS),另一种是基于判别灵敏度的线性学习方法(SBLLM)。韵律特征的使用已经被用于大量的实际应用,包括语音相关的应用,如说话人和单词识别,情感和口音识别,主题和句子分割,以及文本到语音的应用。在本文中,我们继续特别关注阿拉伯语,因为其他语言在这方面受到了很多关注。此外,我们的目标是通过应用上述两种强大的分类方法来提高我们之前使用的技术的性能,其中支持向量机(SVM)方法是性能最好的。记录的连续语音首先使用能量和时间持续参数分割成句子。然后从每个句子中提取韵律特征,并将其输入到两个提出的分类器中,从而将每个句子分类为疑问句或非疑问句。我们基于一个中等规模的数据库进行了广泛的模拟工作,结果表明,这两种分类器在所有实验中都优于SVM,其中type-2 FLS分类器始终表现出最佳性能,因为它能够处理所有形式的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches
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.
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