Hridayesh Lekhak

Hridayesh Lekhak

Junior Computer Science student at UT Arlington, interested in Machine Learning, Natural Language Processing, and full-stack development.

Publications

You can also find my articles on my Google Scholar profile.

A Data-driven Approach to the Longitudinal Study of Canine Vocal Pattern Development

Published in Proceedings of the 33rd ACM International Conference on Multimedia, 2025

This paper presents a data-driven approach to the longitudinal study of canine vocal pattern development.

Recommended citation: Lekhak, Hridayesh and Wang, Theron S. and Dang, Tuan M. and Zhu, Kenny Q. (2025). A Data-driven Approach to the Longitudinal Study of Canine Vocal Pattern Development. In Proceedings of the 33rd ACM International Conference on Multimedia (MM \"25).
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Toward Automatic Discovery of a Canine Phonetic Alphabet

Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Dogs communicate intelligently but little is known about the phonetic properties of their vocalization communication. For the first time, this paper presents an iterative algorithm inspired by human phonetic discovery, which is based on minimal pairs that determine phonemes by distinguishing different words in human language, and is able to produce a complete alphabet of distinct canine phoneme-like units.

Recommended citation: Wang, T. S., Li, X., Lekhak, H., Dang, T. M., Wu, M., & Zhu, K. Q. (2025, July). Toward Automatic Discovery of a Canine Phonetic Alphabet. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 9207-9219).
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DogSpeak: A Canine Vocalization Classification Dataset

Published in ACM MM 25 (Datasets Track), 2025

This paper introduces DogSpeak, a dataset for canine vocalization classification.

Recommended citation: Hridayesh Lekhak, Theron S. Wang, Tuan M. Dang, and Kenny Q. Zhu. 2025. DogSpeak: A Canine Vocalization Classification Dataset. In Proc. ACM MM 25 (Datasets Track).
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