مفتی محمد تقی عثمانی کی معروف تصنیفات و تالیفات کا تعارفی جائزہ

Mufti Muhammad Taqi Usmani is one of the leading Deoband Hanafi Islamic Scholars living today from Pakistan. He is the son of late Molana Muhammad Shafi, the grand mufti of Pakistan. He is the brother of Islamic scholars Muhammad Rafi Usmani, Muhammad Wali Razi, Muhammad Razi Usmani as well as of Urdu poet Muhammad Zaki Kaifi. He is regarded as an expert in the fields of Hadith, Islamic Jurisprudence (Fiqh), Economics and Tasawwuf. He served as a judge on the Federal Shariat Court of Pakistan from 1980 to 1982 and the Shariat Appellate Bench of the Supreme Court of Pakistan between 1982 and 2002. He is generally known as one of the leading Shariah Scholars active in the field of Islamic finance. For more than a decade he has served as Chairman or Member of Shariah Supervisory boards of a dozen Islamic banks and prestigious financial institutions in various parts of the world. Allah Almighty has blessed him with the writing skill. He has written translations of the Holy Quran in both English and Urdu. He has been writing on various Islamic topics in Arabic, Urdu & English and is author of more than 70 books and numerous articles, published in a number of journals and magazines. In his books, Justice Taqi Usmani has discussed the solutions of individual, collective, social, political and economic problems in the light of Islamic principles. His books are very famous not only in Pakistan but also in India, Malaysia, Bangladesh and many other countries in the world. With this perspective, the present article deals with the introduction of important books of Mufti Muhammad Taqi Usmani.

Automatic Segmentation of Speech

Time-aligned and labeled speech at sub-word level is required to develop spoken language technology components. Determining time boundaries of sub word units of speech and labelling those, is the speech segmentation problem. Manual human-labeling is considered to be the most accurate, which however requires significant amount of time when large amount of speech has to be dealt. The evidences which humans employ are based on knowledge of acoustic-phonetics and at very basic level works on spectrograms based techniques. Based on a hypothesis that computers can also segment speech automatically if evidence which human experts utilizes are used, leads us towards time effective automatic speech segmentation. In this thesis unsupervised automatic time-alignment of speech at sub-word level is carried out based on the pieces of information which spectrograms carry. The speech spectrogram engineered in this thesis does not possess information of vocal excitations and capture dynamics of vocal tract only. The novel feature is found suitable for segmentation problem and utilizes both forward and inverse characteristics of vocal tract (FICV). Additionally to evaluate the suitability of a feature extraction technique for speech segmentation task, a framework has also been developed. In the thesis, speech segmentation is carried out on indigenously developed Classical Arabic (CA) dataset and therefore becomes first scheme of its kind for CA which is an under resourced language in speech technology. The performance of FICV based speech segmentation scheme is compared and shown to be significantly better than standard unsupervised and supervised techniques both in terms of error-rates and alignment accuracies. Reduction of 12.29% in error rates is achieved with FICV based feature when compared with standard unsupervised technique. Carrying out supervised segmentation requires a basic sub-word level recognizer, which labels and aligns speech. In this connection a Hidden Markov Model (HMM) based speech recognizer is trained. The acoustic modeling is carried using a discriminative technique which shows better recognition accuracies of up to 4% than the non-discriminative technique. Thesis also verifies that using manually-labeled data for training acoustic models can further improve recognition accuracies by 3-4%. In this regard, thesis carries details of experimental steps which can also serve as guideline for developing an automatic speech recognizer for CA.