# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 3 Results of the Systematic Review
### Solutions Based on Speech-to-Text and Text-to-Speech  
Speech-to-text technology has seen significant advancements in accuracy, largely due to improvements in natural language processing and deep learning models. It is widely used in applications like transcription services (e.g., Otter.ai and Google Live Transcribe), virtual assistants (e.g., Siri, Alexa), and closed captioning for accessibility in media. Real-world challenges include handling diverse accents, noisy environments, and domain-specific terminology, which can affect user satisfaction and the overall effectiveness of STT. Text-to-speech systems have advanced to generate more natural and expressive voices. These systems are now integral to assistive technologies like screen readers for visually impaired users (e.g., JAWS and NVDA) and language learning apps (e.g., Duolingo). TTS are integrated in many real-world applications such as GPS systems that provide spoken directions, virtual assistants (e.g., Siri, Google Assistant, Alexa), and accessibility tools for individuals with visual impairments (e.g., screen readers like JAWS and VoiceOver). Recent advancements in TTS technology have led to remarkable accuracy and performance levels, particularly in models like Tacotron 2, which achieved an MOS of 4.38, and Tacotron 1, which produced an MOS of 4.01 [15]. Additionally, an almost unsupervised learning method for TTS aimed at low-resource languages demonstrated exceptional results, with a 99.84% word-level intelligibility but a 2.68 MOS on the LJSpeech dataset, despite utilizing only 200 paired data points and additional unpaired data [16]. [PERSON], [PERSON] et al. present \"LibriTTS\", a speech corpus designed for TTS applications, based on the LibriSpeech dataset. It includes 585 h of 24 kHz speech data from 2456 speakers. Models trained on LibriTTS achieved mean opinion scores (MOS) above 4.0 for naturalness in five out of six evaluation speakers [17]. [PERSON], [PERSON] et al. explore applications that facilitate text and speech communication, including SR, STT conversion, synthetic speech generation, and language translation. The focus is on the methods employed to ensure seamless interaction between parties across networks [18]. In the same context, [PERSON], [PERSON] et al. introduce a blind-friendly user interface framework that simplifies interface components based on user profiles and context. An empirical study with 41 participants demonstrated significant improvements in their smartphone interaction experience [19]. [PERSON], [PERSON] et al. present an object detection and navigation system for visually impaired individuals, featuring static and dynamic obstacle detection. The static module uses techniques like SURF feature extraction, while the dynamic module employs Gaussian mixture models [20]. Visual information is converted into audio feedback via a Raspberry Pi, including vibrations for users with partial hearing loss. The system effectively models backgrounds, eliminates shadows, and supports blind users in navigating their environment. Figure 3: The trending of the keyword “speech recognition” over the last 15 years (November 2009–November 2024) from Google Trends. [PERSON], [PERSON] et al. explore the integration of small-sized computers with STT technology, particularly addressing the challenge of implementing STT algorithms on microcontroller-based systems without compromising performance. A methodology is presented to evaluate STT services using similarity and speed as metrics, with an experiment conducted on a Raspberry Pi 3 for Spanish speakers. The results reveal that Google's STT system outperformed both IBM and Microsoft, showing faster conversion times and higher accuracy, with a similarity score of 97.116%, compared to Microsoft's 90.825% and IBM's 90.337% [21]. Following the experiments of other researchers in this field, [PERSON], [PERSON] et al. created a literature review where they discuss the process of TTS synthesis, which converts natural-language text into speech using recorded samples stored in a database.