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arXiv · 2501.11065

Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets

Or Haim Anidjar·Roi Yozevitch
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Abstract

In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting ten languages across Indo-European, Semitic, and East Asian families. The major innovation involved optimizing the architecture of Time Delay Neural Networks. We introduced additional layers and restructured these networks into a funnel shape, enhancing their ability to process complex linguistic patterns. A rigorous grid search determined the optimal settings for these networks, significantly boosting their efficiency in language pattern recognition from audio samples. The model underwent extensive training, including a phase with augmented data, to refine its capabilities. The culmination of these efforts is a highly accurate system, achieving a 97\% accuracy rate in language recognition. This advancement represents a notable contribution to artificial intelligence, specifically in improving the accuracy and efficiency of language processing systems, a critical aspect in the engineering of advanced speech recognition technologies.

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Or Haim Anidjar, Roi Yozevitch. 2025-01-19. Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets. https://arxiv.org/abs/2501.11065

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