Speaker's Image Research Articles

Стратегия принятия предложения в политическом дискурсе

This study reviewed several previous studies related to political discourse, and among them, special attention was paid to the research of N. Fairclough and I. Fairclough. It has been argued that the acceptance strategy discourse has a distinctive argument structure as opposed to the general argument discourse. Putin's speech to the Russian Parliament was analyzed. In Putin's speech, it was found that in the discourse of acceptance strategy, the utterance motive, the proposition premise, and the proposition premise considerations can function as categories of arguments. Putin elaborated on the content of the proposal and at the same time suggested that the proposal was a practical and broad issue, stating that there was such a public opinion among citizens; however, Putin's speech emphasized that it is inappropriate to exclude the presidential term from the constitution without expressing his intention to accept the proposal directly; however, he expressed his opinion on the argument that would allow the incumbent to run for the next presidential election. He presented 1) substantive premises that the current Russian constitutional system is weak and there is an active debate about the presidential system; 2) the value premise that the amendment bill would strengthen our sovereignty, traditions and values; 3) alternatives that the parliamentary cabinet system not suitable for the current stage. This series of structures of argumentation has been seen to emphasize that Tereshkova's proposal is significant. In this study, a discourse strategy that reinforces the validity of a proposal without explicitly making a claim was named unrevealed statements. This strategy was considered to have the advantage of maximizing the effect of the utterance, providing only a persuasive argument without compromising the speaker's image in any way.

A Semantic and Rhetorical Study of Manipulation in Two English and Arabic Political Speeches

Manipulation is a discursive phenomenon used by speakers to affect the thoughts ( and indirectly the actions) of the recipients. This study is concerned with manipulation in two political speeches; one in English delivered by the American President Donald J. Trump, while the other in Arabic delivered by the Iraqi President Barham Salih to be the study's data. Each one of these two speeches is divided into serial-numbered extracts( henceforth Ext.). The study aims at investigating the semantic and rhetorical devices utilized as manipulation strategies in these speeches. To this end, the qualitative and quantitative methods of analysis will be followed in this study. The significance of the study stems from how the ideological dimension based on bettering off the speaker's image and derogating others' image plays a vital role in the political speeches. This study draws on Van Dijk's ideological approach to Critical Discourse Analysis (CDA) of political discourse, and accordingly, it is adopted as a model. Results revealed that both speakers use lexicalization, a list of three, repetition, and citing as effective techniques in their two speeches to affect their recipients' minds. The study concluded that the ideological framework of positive self-presentation and negative other-presentation is the central umbrella under which manipulation can exist and work freely. The findings might help linguists and political analysts to understand how politicians use the linguistic features in their discourse to affect the audience's thoughts and behaviors manipulatively.

A Semantic and Rhetorical Study of Manipulation in Two English and Arabic Political Speeches

Manipulation is a discursive phenomenon used by speakers to affect the thoughts ( and indirectly the actions) of the recipients. This study is concerned with manipulation in two political speeches; one in English delivered by the American President Donald J. Trump, while the other in Arabic delivered by the Iraqi President Barham Salih to be the study's data. Each one of these two speeches is divided into serial-numbered extracts( henceforth Ext.). The study aims at investigating the semantic and rhetorical devices utilized as manipulation strategies in these speeches. To this end, the qualitative and quantitative methods of analysis will be followed in this study. The significance of the study stems from how the ideological dimension based on bettering off the speaker's image and derogating others' image plays a vital role in the political speeches. This study draws on Van Dijk's ideological approach to Critical Discourse Analysis (CDA) of political discourse, and accordingly, it is adopted as a model. Results revealed that both speakers use lexicalization, a list of three, repetition, and citing as effective techniques in their two speeches to affect their recipients' minds. The study concluded that the ideological framework of "positive self-presentation" and "negative other-presentation" is the central umbrella under which manipulation can exist and work freely. The findings might help linguists and political analysts to understand how politicians use the linguistic features in their discourse to affect the audience's thoughts and behaviors manipulatively.

Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

Automatic speech segmentation as an important part of speech recognition system (ASR) is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker's face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker's image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.