Advances in Linguistic Technology: A Corpus Analysis of Speech Recognition Software and Its Limitations

Abstract

This research explores the advances in linguistic technology, specifically focusing on speech recognition software and its inherent limitations through a comprehensive corpus analysis. As speech recognition systems become increasingly integrated into everyday technology, understanding their linguistic capabilities and shortcomings is essential for improving user experience and functionality. The study utilizes a diverse corpus of spoken language data to evaluate the accuracy, contextual understanding, and adaptability of various speech recognition tools. Key findings highlight both the advancements in recognizing diverse accents and dialects, as well as persistent challenges in handling homophones, idiomatic expressions, and background noise. Furthermore, the analysis reveals the impact of linguistic diversity on software performance, emphasizing the need for ongoing refinement in algorithms and training datasets. By identifying these limitations, the research underscores the importance of user feedback and iterative development in enhancing the effectiveness of speech recognition technology. This study contributes to the broader discourse on human-computer interaction and the role of linguistic technology in shaping communication in the digital age.

Keywords: speech recognition, linguistic technology, corpus analysis, limitations, human-computer interaction, dialect diversity, accuracy, user experience.