4 citations · 10 across the 5 of their papers we have counts for
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Svarah: Evaluating English ASR Systems on Indian Accents
Tahir Javed, Sakshi Joshi, Vignesh Nagarajan +6
India is the second largest English-speaking country in the world with a speaker base of roughly 130 million. Thus, it is imperative that automatic speech recognition (ASR) systems…
Vistaar: Diverse Benchmarks and Training Sets for Indian Language ASR
Kaushal Santosh Bhogale, Sai Sundaresan, Abhigyan Raman +3
Improving ASR systems is necessary to make new LLM-based use-cases accessible to people across the globe. In this paper, we focus on Indian languages, and make the case that divers…
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages
Kaushal Santosh Bhogale, Abhigyan Raman, Tahir Javed +4
End-to-end (E2E) models have become the default choice for state-of-the-art speech recognition systems. Such models are trained on large amounts of labelled data, which are often n…
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
Tahir Javed, Kaushal Santosh Bhogale, Abhigyan Raman +3
A cornerstone in AI research has been the creation and adoption of standardized training and test datasets to earmark the progress of state-of-the-art models. A particularly succes…
Towards Building ASR Systems for the Next Billion Users
Tahir Javed, Sumanth Doddapaneni, Abhigyan Raman +5
Recent methods in speech and language technology pretrain very LARGE models which are fine-tuned for specific tasks. However, the benefits of such LARGE models are often limited to…