Voice Technology and AI: Innovative Tools for Developing Inclusive Education, Disability Learning, and Mental Health Support - Anand Kunduri, Vijay (Digital Repository of Theses - SSBM Geneva, 2024)
This dissertation tackles a major obstacle in Natural Language Processing (NLP) related to the analysis of unstructured and noisy utterances, specifically in Indian regional languages. The training of NLP algorithms on corpora primarily available in languages like English, French, and Chinese has limited applicability to regional languages due to the lack of relevant corpora. This issue becomes more pronounced in real-world scenarios where the speech is often distorted by background noise and non-sophisticated Automatic Speech Recognition (ASR) engines. To address this problem, the dissertation proposes an innovative solution that uses ensemble methods and focuses on the accuracy of uttered sentences. The approach combines rule-based Machine Learning and fuzzy logic-based algorithms to overcome language variances and accurately identify full or partial matches of actual answers within noisy utterances. The methodology is adapted based on the language of the uttered answers. For regional languages, the dissertation employs sequence matching and fuzzy matching algorithms, with special consideration for answers containing numbers through the utilization of number-to-word methods. In the case of English answers, the methodology involves word form, fuzzy logic, and distance-based similarity matching. The detailed description of the proposed solution emphasizes the inventive aspects contributing to its efficacy. While specific diagrams or figures illustrating the components are not provided, the absence of patented algorithms in the literature or prior art section underscores the novelty of the proposed solution, presenting a distinct and valuable contribution to NLP for regional languages. In summary, this dissertation presents a groundbreaking approach to address the challenges posed by unstructured and noisy utterances in NLP, particularly in Indian regional languages. The ensemble of algorithms and language-specific methodologies positions it as a novel and promising solution capable of significantly enhancing the accuracy of understanding and matching uttered sentences.