[PDF] Cross Word Modeling For Arabic Speech Recognition eBook
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Cross-Word Modeling for Arabic Speech Recognition utilizes phonological rules in order to model the cross-word problem, a merging of adjacent words in speech caused by continuous speech, to enhance the performance of continuous speech recognition systems. The author aims to provide an understanding of the cross-word problem and how it can be avoided, specifically focusing on Arabic phonology using an HHM-based classifier.
This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling. This book comprises 3 sections and thirteen chapters written by eminent researchers from USA, Brazil, Australia, Saudi Arabia, Japan, Ireland, Taiwan, Mexico, Slovakia and India. Section 1 on speech recognition consists of seven chapters. Sections 2 and 3 on speech enhancement and speech modeling have three chapters each respectively to supplement section 1. We sincerely believe that thorough reading of these thirteen chapters will provide comprehensive knowledge on modern speech recognition approaches to the readers.
The book elaborates selected, extended and peer reviewed papers on Communication and Signal Proceesing. As Vol. 8 of the series on "Advances on Signals, Systems and Devices" it presents main topics such as: content based video retrieval, wireless communication systems, biometry and medical imaging, adaptive and smart antennae.
Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP) to adapt existing phonemic MSA acoustic models with a small amount of dialectal ECA speech data. Speech recognition results indicate a significant increase in recognition accuracy compared to a baseline model trained with only ECA data.
This book encompasses a collection of topics covering recent advances that are important to the Arabic language in areas of natural language processing, speech and image analysis. This book presents state-of-the-art reviews and fundamentals as well as applications and recent innovations.The book chapters by top researchers present basic concepts and challenges for the Arabic language in linguistic processing, handwritten recognition, document analysis, text classification and speech processing. In addition, it reports on selected applications in sentiment analysis, annotation, text summarization, speech and font analysis, word recognition and spotting and question answering.Moreover, it highlights and introduces some novel applications in vital areas for the Arabic language. The book is therefore a useful resource for young researchers who are interested in the Arabic language and are still developing their fundamentals and skills in this area. It is also interesting for scientists who wish to keep track of the most recent research directions and advances in this area.
This book constitutes the refereed proceedings of the 7th Asia Information Retrieval Societies Conference AIRS 2011, held in Dubai, United Arab Emirates, in December 2011. The 31 revised full papers and 25 revised poster papers presented were carefully reviewed and selected from 132 submissions. All current aspects of information retrieval - in theory and practice - are addressed; the papers are organized in topical sections on information retrieval models and theories; information retrieval applications and multimedia information retrieval; user study, information retrieval evaluation and interactive information retrieval; Web information retrieval, scalability and adversarial information retrieval; machine learning for information retrieval; natural language processing for information retrieval; arabic script text processing and retrieval.
This book features the latest theoretical results and techniques in the field of guidance, navigation, and control (GNC) of vehicles and aircraft. It covers a range of topics, including, but not limited to, intelligent computing communication and control; new methods of navigation, estimation, and tracking; control of multiple moving objects; manned and autonomous unmanned systems; guidance, navigation, and control of miniature aircraft; and sensor systems for guidance, navigation, and control. Presenting recent advances in the form of illustrations, tables, and text, it also provides detailed information of a number of the studies, to offer readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the development of GNC, making it a valuable resource for both beginners and researchers wanting to further their understanding of guidance, navigation, and control.
This book constitutes the refereed proceedings of the 11th International Conference on Text, Speech and Dialogue, TSD 2008, held in Brno, Czech Republic, September 8-12, 2008. The 79 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 173 submissions. The topics of the conference include, but are not limited to, text corpora and tagging; transcription problems in spoken corpora; sense disambiguation; links between text and speech oriented systems; parsing issues; parsing problems in spoken texts; multi-lingual issues; multi-lingual dialogue systems; information retrieval and information extraction; text/topic summarization; machine translation; semantic networks and ontologies; semantic web; speech modeling; speech segmentation; speech recognition; search in speech for IR and IE; text-to-speech synthesis; dialogue systems; development of dialogue strategies; prosody in dialogues; emotions and personality modeling; user modeling; knowledge representation in relation to dialogue systems; assistive technologies based on speech and dialogue; applied systems and software; facial animation; and visual speech synthesis
Significant advances have been made with Modern Standard Arabic (MSA) Automatic Speech Recognition (ASR) applications. Yet, dialectal conversation ASR is still trailing behind due to limited language resources. As is the case in most cultures, the formal Modern Standard Arabic language is not used in daily life. Instead, varieties of regional dialects are spoken, which creates a dire need to address dialect ASR systems. Processing MSA language naturally poses considerable challenges that are passed on to the processing of its derived dialects. In dialects, many words have gradually morphed from MSA pronunciations and at many times have different usages. Also, a significant number of new vocabulary words have been imported from other foreign languages. In addition to these issues, dialects have low resources to be considered for any meaningful natural language processing (NLP) research. Therefore, there is a pressing need for an efficient language model (LM) for deployment in Arabic conversational speech recognition systems. In this thesis, we explore building an Iraqi dialect conversational speech language model based on utilizing MSA data. Because there isn't a pre-defined annotated vocabulary set, our main approach is making use of word embedding for unsupervised clustering of the MSA-Iraqi dialect words. Clustering the dialect words within the relative MSA words is employed to create a class-based LM. This allows the use of MSA data to cover the insufficiency of the dialect data. The model uses the dialect word's statistical history in addition to the statistics of related MSA words to make predictions of the intended spoken word sequence. Thus, efficient word embedding becomes important to produce a reliable LM. To achieve efficient word embedding, first an analysis of the MSA and the Iraqi dialect vocabulary sets and their context intersection is conducted. For this purpose, Dialect Fast Stemming Algorithm (DFSA) is proposed that utilizes the MSA data and a predefined dialect suffixes set. The intersection set enlarged from 42.8% to 54% of the Iraqi vocabulary, and from 8% to 13% of the MSA vocabulary. Second, the syntax and semantic feature vector that is produced by applying the distributional-theory-based word embedding word2vec contained noise from having contexts that appear in MSA or in the dialect solely; thus, applying PCA reduced the perplexity (pp) by 6.7%. Finally, the novel Wasf-Vec topological word embedding algorithm is proposed, which relies on the hypothesis that for a rich morphological language like Arabic, the word’s topological feature is of much significance to be considered. This new feature extraction technique addresses the high morphological properties and reduces PP by 7% when using distributional-theory-based word embedding. Moreover, a deep analysis of the words syntagmatic and paradigmatic relations are illustrated based on solid Arabic and Greek linguistic theories that prove the need of topological word embedding. The three researches compiling this dissertation demonstrate the feasibility of utilizing MSA resources to enhance dialect processing. Further, combining distributional-theory-based and Topology-based word embedding is highly of great intense for future investigation.