Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.

Ḥalāl linguistic meaning understanding among Non-Muslim in Malaysia

Ḥalāl discourse is not only familiar to Muslims, but also non-Muslims in Malaysia. This study discusses the response from non-Muslim in Malaysian ḥalāl discourse, with the objectives of identifying the ḥalāl linguistic meaning among non-Muslim in Malaysia. The discussion of meaning is based on the language interpretation which used in Sunni pragmatic research, such as how language is perceived either literal or figurative meanings based Mohamed & Yunis (2013) and Russell (1940) approach that focuses on the meaning and fact in his language theory. The data in the discussion related to the non-Muslims response towards 5 categories of ḥalāl implementation, namely ḥalāl food, ḥalāl certification, ḥalāl sign, ḥalāl name or brand of the product and ḥalāl supply chain. The data were the controversial ḥalāl issues from 2014 to 2018 taken from local newspapers such as Star Online. The discussion demonstrates that the understanding of non-Muslims linguistically can be traced from the keywords, such as understanding, compliance, awareness, acceptance and recognised which are denotatively having positive meanings. However, there are other words denotatively having negative meanings such as confusion and sensitivity. The result also shows ḥalāl does not only concern Muslims but non-Muslims as well. In principle, Islam does not prohibit non-Muslims from consuming the products offered based on guidelines recommended in Islam. The findings reveal that ḥalāl understanding in Malaysia still needs to be strengthened among non-Muslims. Therefore, the understanding and knowledge of ḥalāl implementation is the main pillar in maintaining the relationship between Muslims and non-Muslims in this society.

Covert societal practices in linguistic choices of Pakistani editorials: Fairclough’s CDA model approach

Purpose: Core concept of CDA revolves around the interdependency of description of language, interpretation of language and prevailing discursive practices in society. This study examines the role a language plays in formulating ideological subjective position of male and female in any contemporary society. Methodology: The study applied Fairclough’s CDA model on the Pakistani editorials of two English newspapers in order to qualitatively analyze the linguistic choices and its placement in the text by focusing on other modalities of language such as adverbs, adjectives, metaphors and action verbs for unraveling the patriarchal practices of the society in which women are at the stake of more disadvantages than the men in COVID-19 pandemic situation. Findings: Editorials showed remarkable use of differences in linguistic choices for depiction of powerful and powerless group. Findings showed the existence of hegemony in society where men dominates women by violating their basic rights. Abusive nature of men during the pandemic situation has transformed women's tendency to attend their work through online sources into a tiresome experience. Linguistic choice and syntactic structure have brought forth the discourse type, situational context and societal practices at the surface level which in turns connect back to the role of society and culture in shaping the perception of editors in writing a piece of editorials. Unique Contribution to Practice and Policy: Two newspapers have adopted different stances in depicting a same issue which also reveals the hidden ideology of the press media in which they use implicit or explicit tone in delivering their ideas. This research can help in exploring new dimensions of Pakistani editorials’ language where different tone of language represent unique subject position of female and male in pandemic situation.

Sign Language Interpreter using Deep Learning

Sign Language is invaluable to hearing and speaking impaired people and is their only way of communicating among themselves. However, it has limitations with its reach as the rest of the people have no information regarding sign language interpretation. Sign language is communicated via hand gestures and visual modes and is therefore used by hearing and speaking impaired people to intercommunicate. These languages have alphabets and grammar of their own, which cannot be understood by people who have no knowledge about the specific symbols and rules. Thus, it has become essential for everyone to interpret, understand and communicate via sign language to overcome and alleviate the barriers of speech and communication. This can be tackled with the help of machine learning. This model is a Sign Language Interpreter that uses a dataset of images and interprets the sign language alphabets and sentences with 90.9% accuracy. For this paper, we have used an ASL (American Sign Language) Alphabet. We have used the CNN algorithm for this project. This paper ends with a summary of the model’s viability and its usefulness for interpretation of Sign Language. Keywords: Sign Language, Machine Learning, Interpretation model, Convoluted Neural Networks, American Sign Language

How do 3-year-olds use relevance inferencing to interpret indirect speech?

If a child asks a friend to play football and the friend replies, ‘I have a cough’, the requesting child must make a ‘relevance inference’ to determine the communicative intent. Relevance inferencing is a key component of pragmatics, that is, the ability to integrate social context into language interpretation and use. We tested which cognitive skills relate to relevance inferencing. In addition, we asked whether children’s lab-based pragmatic performance relates to children’s parent-assessed pragmatic language skills. We tested 3.5- to 4-year-old speakers of British English (Study 1: N = 40, Study 2: N = 32). Children were presented with video-recorded vignettes ending with an utterance requiring a relevance inference, for which children made a forced choice. Study 1 measured children’s Theory of Mind, their sentence comprehension and their real-world knowledge and found that only real-world knowledge retained significance in a regression analysis with children’s relevance inferencing as the outcome variable. Study 2 then manipulated children’s world-knowledge through priming but found this did not improve children’s performance on the relevance inferencing task. Study 2 did, however, reveal a significant correlation between children’s relevance inferencing and a measure of morpho-syntactic production. In both studies parents rated their children’s pragmatic language usage in daily life, which was found to relate to performance in our lab-based relevance inferencing task. This set of studies is the first to empirically demonstrate that lab-based measures of relevance inferencing are reflective of children’s pragmatic abilities ‘in the wild’. There was no clear association between relevance inferencing and Theory of Mind. There was mixed evidence for the role of formal language, which should be further investigated. Finally, real-world knowledge was indeed associated with relevance inferencing but future experimental work is required to test causal relations.