  In this paper, we present a 170.83 hour Indian English spontaneous speech
dataset. Lack of Indian English speech data is one of the major hindrances in
developing robust speech systems which are adapted to the Indian speech style.
Moreover this scarcity is even more for spontaneous speech. This corpus is
crowd sourced over varied Indian nativities, genders and age groups.
Traditional spontaneous speech collection strategies involve capturing of
speech during interviewing or conversations. In this study, we use images as
stimuli to induce spontaneity in speech. Transcripts for 23 hours is generated
and validated which can serve as a spontaneous speech ASR benchmark. Quality of
the corpus is validated with voice activity detection based segmentation,
gender verification and image semantic correlation. Which determines a
relationship between image stimulus and recorded speech using caption keywords
derived from Image2Text model and high occurring words derived from whisper ASR
generated transcripts.
