CASPER: A Large Scale Spontaneous Speech Dataset
This addresses a key bottleneck for researchers developing speech processing capabilities by providing a reproducible framework for collecting spontaneous speech data, though it is incremental as it builds on existing data collection needs.
The paper tackles the scarcity of high-quality spontaneous speech data for speech processing by presenting a novel pipeline to elicit and record natural dialogues, resulting in a dataset with over 100 hours of spontaneous speech.
The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous speech data, as most existing datasets contain scripted dialogues. To address this, we present a novel pipeline for eliciting and recording natural dialogues and release our dataset with 100+ hours of spontaneous speech. Our approach fosters fluid, natural conversations while encouraging a diverse range of topics and interactive exchanges. Unlike traditional methods, it facilitates genuine interactions, providing a reproducible framework for future data collection. This paper introduces our dataset and methodology, laying the groundwork for addressing the shortage of spontaneous speech data. We plan to expand this dataset in future stages, offering a growing resource for the research community.