Paper Title
Neural Speech Segmentation for Multilingual Audio Content

Abstract
The rapid proliferation of multilingual audio content in digital media has necessitated increased interest in having robust, language-independent, and accurate speech segmentation systems. Traditional speech segmentation mechanisms tend to use language-specific rule-based or statistical-based models and lack the capacity to generalize between multiple languages and dialects of languages, in addition to noise-contaminated real-world signals. This article proposes a novel deep learning speech segmentation framework, which can operate successfully on multilingual audio content without any constraint imposed by language. Our suggested architecture employs a Temporal Convolutional Network (TCN) supplemented with multiscale feature aggregation, attention, and language-aware normalization to identify and isolate speech portions, such as speaker turns, pauses, and linguistic units, in an accurate manner. The framework is trained and tested on a vast and diverse corpus that covers more than 10 language families and several acoustic domains, with both supervised and selfsupervised learning approaches being employed. Experimental results show cutting-edge segmentation accuracy, strong generalization to unseen languages, and real-time processing, beating current baselines in both clean and noisy settings. Ablation studies show the significance of multi-scale processing and language normalization in attaining high accuracy. The suggested model establishes a new standard for multilingual speech segmentation and opens up avenues for further research in universal speech processing with applications in live subtitling, speaker diarization, and assistive technology. Keywords- Neural speech segmentation,Temporal convolutional networks (TCN), Speech boundary detection, Multilingual audio processing, Deep Learning,Speech recognition