A Comparative Study of Dawn and Jang with Special Regard to Agenda Setting Theory of Press: Ten Years before and after 9/11

The relationship between Pakistan and America has always been a focus of the media of both countries as well as the international media. Even a slight shift in the policies of these two countries for each other is capable of making newspaper headlines and attracting maximum attention of the press of both countries. This study is a comparative analysis of the editorial pages of daily Dawn (English) and daily Jang (Urdu) to explore the agenda setting role of two of Pakistan’s oldest and most credible newspapers, vis-à-vis the Pakistan-US relationship. The research takes into consideration a total of 20 years of Pakistan-US relations by dividing the period into two groups which are ten years before the incident of 9/11 and ten years after 9/11. This understudied period is specifically important because the relations between Pakistan and America during these phases have vacillated between periods of engagements, wherein Pakistan enjoyed the status of the most favoured ally without compromising its regional interest, and the periods of disengagements wherein Pakistan faced sanctions from the US and was left alone to deal with the aftermath of the Afghan war and the War on Terrorism. Hence, this study obtains interesting insights about how the two Pakistani newspapers which represent the Urdu and the English press of Pakistan highlighted the agenda-setting role of the press through the coverage of the issues between Pakistan and US on their editorial pages.

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.