This paper reviewed the sign language research in the vision-based hand gesture recognition system from 2014 to 2020. Its objective is to identify the progress and what needs more attention. We have extracted a total of 98 articles from well-known online databases using selected keywords. The review shows that the vision-based hand gesture recognition research is an active field of research, with many studies conducted, resulting in dozens of articles published annually in journals and conference proceedings. Most of the articles focus on three critical aspects of the vision-based hand gesture recognition system, namely: data acquisition, data environment, and hand gesture representation. We have also reviewed the performance of the vision-based hand gesture recognition system in terms of recognition accuracy. For the signer dependent, the recognition accuracy ranges from 69% to 98%, with an average of 88.8% among the selected studies. On the other hand, the signer independent’s recognition accuracy reported in the selected studies ranges from 48% to 97%, with an average recognition accuracy of 78.2%. The lack in the progress of continuous gesture recognition could indicate that more work is needed towards a practical vision-based gesture recognition system.

Sentiment analysis (SA), also referred to as opinion mining, has become a widely used real-world application of Natural Language Processing in recent times. Its main goal is to identify the hidden emotions behind the plain text. SA is especially useful in e-commerce fields, where comments and reviews often contain a wealth of valuable business information that has great research value. The objective of this study is to examine the techniques used for SA in current e-commerce platforms as well as the future directions for SA in e-commerce. After examining the existing systematic review papers, it was found that there is a lack of a single comprehensive review paper that addresses research questions. The findings of this study can provide researchers in the field of SA with a comprehensive understanding of the current techniques and platforms utilized, as well as provide insights into the future directions. Through the utilization of specific keywords, we have identified 271 papers and have chosen 54 experimental papers for review. Among these, 26 papers (representing 48.%) have exclusively employed machine Learning techniques, while 24 (44.%) have looked into addressing SA through deep learning techniques, and 4 (7.%) have employed a hybrid approach using both machine learning and deep learning techniques. Additionally, our review revealed that Amazon and Twitter emerged as the two most favored data sources among researchers. Looking ahead, promising research avenues to include the development of more universal language models, aspect-based SA, implicit aspect recognition and extraction, sarcasm detection, and fine-grained sentiment analysis.

The automatic speech recognition (ASR) system is increasingly being applied as assistive technology in the speech impaired community, for individuals with physical disabilities such as dysarthric speakers. However, the effectiveness of the ASR system in recognizing dysarthric speech can be disadvantaged by data sparsity, either in the coverage of the language, or the size of the existing speech database, not counting the severity of the speech impairment. This study examines the acoustic features and feature selection methods that can be used to improve the classification of dysarthric speech, based on the severity of the impairment. For the purpose of this study, we incorporated four acoustic features including prosody, spectral, cepstral, and voice quality and seven feature selection methods which encompassed Interaction Capping (ICAP), Conditional Information Feature Extraction (CIFE), Conditional Mutual Information Maximization (CMIM), Double Input Symmetrical Relevance (DISR), Joint Mutual Information (JMI), Conditional redundancy (Condred) and Relief. Further to that, we engaged six classification algorithms like Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Classification and Regression Tree (CART), Naive Bayes (NB), and Random Forest (RF) in our experiment. The classification accuracy of our experiments ranges from 40.41% to 95.80%.

The advancement of mobile technologies has motivated countries around the world to aim for smarter health management to support senior citizens. However, the use of mobile health applications (mHealth apps) among senior citizens appears to be low. Thus, drawing upon user expectations, the present study examined user requirements for a senior-friendly mHealth application. A total of 74 senior citizens were interviewed to explore the difficulties they encounter when using existing mobile apps. This study followed Nielsen’s usability model to identify user requirements from five aspects, namely learnability, efficiency, memorability, error, and satisfaction. Based on the results, a guideline was proposed pertaining to usability and health management features. This guideline offers suggestions for mHealth app issues related to phrasing, menus, simplicity, error messages, icons and buttons, navigation, and layout, among others. The study also found that speech recognition technology can help seniors access information quickly. The proposed guideline and findings offer valuable input for software and app developers in building more engaging and senior-friendly mHealth apps.

Automatic speech recognition (ASR) is currently used in many assistive technologies, such as helping individuals with speech impairment in their communication ability. One challenge in ASR for speech-impaired individuals is the difficulty in obtaining a good speech database of impaired speakers for building an effective speech acoustic model. Because there are very few existing databases of impaired speech, which are also limited in size, the obvious solution to build a speech acoustic model of impaired speech is by employing adaptation techniques. However, issues that have not been addressed in existing studies in the area of adaptation for speech impairment are as follows: (1) identifying the most effective adaptation technique for impaired speech; and (2) the use of suitable source models to build an effective impaired-speech acoustic model. This research investigates the above-mentioned two issues on dysarthria, a type of speech impairment affecting millions of people. We applied both unimpaired and impaired speech as the source model with well-known adaptation techniques like the maximum likelihood linear regression (MLLR) and the constrained-MLLR(C-MLLR). The recognition accuracy of each impaired speech acoustic model is measured in terms of word error rate (WER), with further assessments, including phoneme insertion, substitution and deletion rates. Unimpaired speech when combined with limited high-quality speech-impaired data improves performance of ASR systems in recognising severely impaired dysarthric speech. The C-MLLR adaptation technique was also found to be better than MLLR in recognising mildly and moderately impaired speech based on the statistical analysis of the WER. It was found that phoneme substitution was the biggest contributing factor in WER in dysarthric speech for all levels of severity. The results show that the speech acoustic models derived from suitable adaptation techniques improve the performance of ASR systems in recognising impaired speech with limited adaptation data.