Developing an AI-Powered System for Dialect Recognition and Preservation

Thesis Abstract

The rapid socio-cultural changes and increased globalization have accelerated the decline of numerous indigenous dialects, posing a significant threat to linguistic diversity and cultural heritage. This study addresses the pressing need for effective technological solutions to recognize and preserve dialectal variations, especially in regions where dialects are under threat of extinction. The primary aim is to develop an AI-powered system capable of accurately recognizing, classifying, and documenting dialectal speech, thereby supporting linguistic preservation and revitalization initiatives. Specific objectives include designing a dialect recognition model using deep learning techniques, evaluating the system's accuracy and robustness, and creating a comprehensive digital repository of dialectal audio samples. The research adopts a mixed-methods design, integrating quantitative and qualitative approaches to ensure a holistic understanding of dialect recognition processes and system performance. The target population comprises speakers of five distinct dialects within the region, totaling approximately 500 individuals, selected through stratified random sampling to capture diverse speech characteristics. Data collection involves recording authentic speech samples using high-fidelity microphones across multiple natural settings, complemented by semi-structured interviews to gather contextual linguistic information. The primary dataset comprises over 2,000 annotated audio files, which are processed through automated speech recognition (ASR) systems and subsequently labeled for dialectal features. The core methodology involves training convolutional neural networks (CNNs) combined with recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) models, to develop a dialect classification algorithm. The study employs transfer learning using pre-trained speech recognition models such as Wav2Vec 2.0, fine-tuned on the collected dialectal datasets to enhance accuracy amid limited data. To evaluate performance, the system's recognition accuracy is measured through metrics like precision, recall, F1-score, and overall classification accuracy, with cross-validation techniques utilized to ensure robustness. Additionally, a thematic analysis of interview transcripts is conducted to contextualize dialectal features and identify sociolinguistic factors influencing dialect variability. Expected findings include a recognition accuracy exceeding 85% for most dialects, demonstrating the system's potential as a reliable tool for dialect identification. The study anticipates revealing significant phonetic, lexical, and tonal markers distinct to each dialect, which enhance classification performance. Moreover, the research is expected to identify specific linguistic features that are most amenable to machine recognition, contributing to the theoretical understanding of dialectal phonetics and sociolinguistics. The digital repository generated through this system aims to serve as a valuable resource for linguists, educators, and community stakeholders engaged in dialect preservation. This research substantially advances existing knowledge by integrating state-of-the-art deep learning techniques with linguistic theory to produce a scalable and accessible tool for dialect recognition and preservation. It extends prior work on speech technology by focusing specifically on under-documented dialects, offering practical solutions for linguistic communities facing endangerment. The study also provides a framework for replicating similar systems in other languages and regions, effectively bridging the gap between computational linguistics and sociolinguistics. Concluding, the study affirms that AI-powered systems can significantly aid in arresting the decline of dialects through accurate recognition and systematic documentation. It recommends the integration of such systems into language revitalization programs and encourages further research into multimodal approaches, incorporating visual and contextual cues to enhance recognition accuracy. Additionally, future studies should explore community-based participatory methods to ensure the culturally sensitive deployment of technological interventions in dialect preservation efforts. This research ultimately envisages a technological paradigm that not only recognizes dialectal diversity but actively promotes its preservation amidst ongoing socio-cultural transformations.