The research project conducted at the University of California, Santa Barbara, under the realm of Data Science and Natural Language Processing (NLP), aimed to enhance the understanding and processing of linguistic patterns and resonance, particularly in the context of language games.
One of the project’s key objectives was to develop sophisticated methods for dissecting the Chinese corpus into distinct intonation units, enabling a more granular analysis of spoken language nuances. This endeavor involved the implementation of advanced data processing techniques and deep linguistic analysis, which were pivotal in refining the approach to language modeling. Manual inspections of the automated separations from native speakers and linguists based on listening to the original audio conversations are crucial steps as well to capture and correct certain errors.
Central to this research was the construction and training of NLP models tailored to process spoken language data effectively. This aspect of the project was instrumental in the development of Rezonator, a prototype resonance-optimizing linguistic application. Rezonator stands as a significant innovation in linguistic technology, offering novel tools for the analysis and enhancement of linguistic resonance. The research team’s contributions in building Rezonator under Prof. John Du Bois showcased not only a proficiency in handling complex datasets and NLP models but also a commitment to advancing the field of language studies through technological solutions. The project thus represents an improvement in the intersection of language analysis and data science, setting a new tool in the application of NLP in understanding and optimizing language resonance.