Designing and Optimizing Deep Learning Models for Speech Recognition in the Albanian Language

Abstract

Recent studies have highlighted the design and optimization of deep learning models tailored for language speech recognition. Utilizing this tool for underrepresented languages, such as Albanian, which features intricate phonetic and syntactic structures and is regarded as a limited resource language, marks a significant step forward in speech recognition technologies within communities that speak it. While highly effective for widely spoken languages like English and Mandarin, languages like Albanian face technological challenges due to insufficient linguistic resources and a lack of research focus. It is crucial to develop speech recognition systems that accurately capture the distinctive linguistic traits of Albanian, taking into account the challenges posed by its various dialects and complex grammar, for the design and optimization of deep learning models. Computational linguistics, which explores deep learning applications in natural language processing (NLP), involves qualitative and quantitative analyses, developing speech datasets, training and testing different deep learning architectures, and employing optimization techniques. These models are assessed using standard speech recognition metrics, such as word error rate (WER) and computational efficiency. In summary, our findings provide a robust, efficient, and scalable framework for Albanian speech recognition, contributing to the broader objective of enhancing language inclusion in AI technologies.