Kaushal Bhogale

Indic DiarBench reveals that existing ASR systems struggle with the rich linguistic diversity of Indian languages, highlighting a critical gap in multilingual speech technology.

Evaluating AudioLLMs reveals that many models fail to leverage contextual information effectively, challenging assumptions about their pretraining capabilities.

Current ASR systems stumble significantly when faced with the nuances of real-world Indian speech, as revealed by a new benchmark exposing geographic performance disparities and the impact of audio quality, speaking rate, and device type.