8 papers
SraVaani 1.0: Scaling Inclusive Speech Recognition for Indic Languages
Sujith Pulikodan, Agneedh Basu, Pavan Kumar J +4
India's linguistic landscape spans over 700 languages and thousands of dialects, yet the vast majority of automatic speech recognition (ASR) systems support only a small fraction o…
Vaani Benchmark V1.0: An Inclusive Multimodal Benchmark Dataset for Hindi
Sujith Pulikodan, Agneedh Basu, Saurabh Kumar +5
Benchmarking is critical for the systematic evaluation and comparison of automatic speech recognition (ASR) systems. While several open-source datasets are available for Hindi ASR,…
Analyzing Language and Geographical Variation in Speech Representations Across 60 Indic Languages
Pavan Kumar J, Agneedh Basu, Pranav Bhat +4
Self-supervised speech encoders are often fine-tuned with language supervision, which can overlook geographical variation. To understand the learned representations under joint sup…
An Analysis of the Effectiveness of Synthetic Speech Data for ASR Fine-tuning in Selected Indic Languages
Sujith Pulikodan, Agneedh Basu, Pavan Kumar +4
Synthetic data has the potential to be a valuable resource for training machine learning models, particularly Automatic Speech Recognition (ASR) Systems; however, its effectiveness…
A study on the impact of region specific data on the performance of Indic ASR
Agneedh Basu, Pavan Kumar J, Pranav Bhat +4
Automatic Speech Recognition (ASR) systems are widely deployed across linguistically diverse regions, yet their ability to generalize across fine-grained geographic variation remai…
Factors affecting ASR performance: A study using state of the art ASR models in Indic Languages
Agneedh Basu, Pavan Kumar J, Pranav Bhat +4
ASR performance varies across languages, speakers, and recording conditions, yet systematic analysis for Indic languages remain limited. We present a large-scale study of decoded o…