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Paper Information

lrec2026-ws-sigul-26

Fine-tuning Whisper with Spontaneous Persian Speech (SPS)

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Title

Fine-tuning Whisper with Spontaneous Persian Speech (SPS)

Abstract

This paper introduces the Spontaneous Persian Speech (SPS) dataset designed for automatic speech recognition (ASR) tasks and a methodology laying the groundwork for addressing the shortage of spontaneous speech data. The corpus aims to support research on natural and conversational Persian, which remains under-represented in current ASR resources. The dataset consists of 694 minutes of audio from a total of 65 speakers, including 34 male and 31 female speakers. It contains 526,585 tokens. The audio segmentation step produces intervals of 1.24 to 3.25 seconds, each containing 3 to 9 words. The recordings cover a variety of environments, from inside cars to homes and shopping areas, including both busy and quiet settings. We use the SPS dataset to fine-tune Whisper and the performance increases significantly for both the small and medium models based on Word Error Rate (WER). This could be an initiative toward building domain-oriented datasets for specific ASR tasks.


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