# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 5 Conclusions  
In order to write this systematic review, we conducted an extensive search across several key academic databases, including Google Scholar, Elsevier, and Springer. This initial search returned thousands of research papers that needed a thorough filtering process. To refine these results and make sure they were closely aligned with the focus of our study, we used the PEDro and PRISMA methodologies. These frameworks allowed us to systematically narrow down the pool of research to obtain more relevant and targeted studies that directly contribute to the objectives of our review. There are numerous studies that have been conducted with a focus on the development of accessibility solutions through the use of artificial intelligence and machine learning technologies, especially in the areas of speech recognition, sign language recognition, and speech-to-text and text-to-speech conversion. These research efforts highlight the growing interest in the field and the accessibility advancements for individuals with disabilities. The primary and most significant findings from this study can be summarized as follows:  
* Speech-to-text and text-to-speech models have impressive accuracy (e.g., in the [PERSON] article, they achieved a 99.84% accuracy of word-level intelligibility for an almost unsupervised algorithm of TTS machine learning [16]; the STT model built by Google demonstrated a similarity score of 97.116% after a robust evaluation [21]);
* [PERSON]'s speech recognition algorithm outperformed other models with a word error rate of 2.9% [7]; the algorithm implemented by [PERSON] for speech recognition using a discrete wavelet transform with a modified MFCC achieved a remarkable recognition rate of 98.86%;
* Continuous sign language recognition needs improvement because the algorithms implemented by authors in the explored solutions did not achieve more than a 95% accuracy ([PERSON] achieved a 93.9% accuracy [35], and [PERSON] achieved a 72.3% accuracy for signed sentences and a 89.5% accuracy for isolated words [3]);
* Research interest in SLR models has increased significantly in the last two years;  
In conclusion, while technologies such as STT, TTS, and SR have achieved an impressive level of maturity, this progress is largely attributed to the high accuracy rates (less than 3.5% word error rates for Whisper and Speechmatics [7]) of these systems and their ability  
Figure 9: Using LLM to translate the output from SLR model. to offer outputs with good mean opinion scores (more than 4.38 mean opinion score for Tacotron 2 [15]), which serves as a key measure of user satisfaction. These advancements have made STT, TTS, and SR solutions highly effective and reliable for a wide range of users, including those with speech and hearing impairments. The accuracy of sign language recognition systems remains a critical area where enhancements could lead to better usability and effectiveness for users. In order to address this challenge, exploring innovative approaches such as AI-generated images for training SLR models could result in insightful and promising results. This method could provide a more diverse dataset, helping, in this way, to capture different variations in sign language. Furthermore, integrating advanced tools like generative large language models such as ChatGPT to rephrase and clarify the output from sign language recognition systems could be a game-changer. Such integration would not only enhance the clarity of communication but also ensure that the expressions used by humans in sign language are accurately transposed to those who may not be fluent in it. A particular challenge lies in enhancing the performance of sign language recognition systems when applied to continuous video streams. Unlike image-based recognition, where individual signs can be accurately identified, video-based recognition requires the system to handle dynamic and fluid sequences of signs, often leading to decreased accuracy.