Development of Speech Corpus for Low-Resource Language- a Case of Sanskrit

Abstract

This paper presents a comprehensive framework for the development of a speech corpus for Sanskrit, designed to facilitate advances in Automatic Speech Recognition (ASR) and AI/ML research. The proposed corpus comprises over 107 hours of transcribed speech data, collected from diverse Sanskrit sources through a systematic and scalable pipeline. We detail the end-to-end methodology adopted for corpus creation, encompassing web crawling, data sanitization, audio downloading, and transcription alignment. Particular emphasis is placed on the methodological rigor applied at each stage, including source selection, preprocessing for quality assurance, transcription protocols, and forced alignment techniques. The paper further addresses the unique complexities inherent to Sanskrit, spanning its phonetic richness, intricate morphological structure, and distinctive syntactic patterns. By systematically addressing these dimensions, the resulting 107-hour corpus aims to serve as a foundational resource for speech technology research in Sanskrit.

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