Introduces a complexity-tiered benchmark for Indic ASR and a reverse multi-stage fine-tuning recipe enabling smaller models to match larger ones on spontaneous speech.
Indicsuperb: A speech processing universal performance benchmark for indian languages
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
4
Pith papers citing it
3
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
Audit of multilingual clinical ASR reveals demographic biases; SamaVaani debiasing technique is proposed to jointly boost performance and fairness in Indian languages.
New categorical error decomposition framework SCRIBE for Indic ASR evaluation, with released rich transcription models for Hindi, Malayalam, and Kannada.
Empirical analysis of speaker and acoustic factors correlated with ASR word error rates across five Indic languages using zero-shot evaluation on multiple open-source models.
citing papers explorer
-
Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition
Introduces a complexity-tiered benchmark for Indic ASR and a reverse multi-stage fine-tuning recipe enabling smaller models to match larger ones on spontaneous speech.
-
SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages
Audit of multilingual clinical ASR reveals demographic biases; SamaVaani debiasing technique is proposed to jointly boost performance and fairness in Indian languages.
-
SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR
New categorical error decomposition framework SCRIBE for Indic ASR evaluation, with released rich transcription models for Hindi, Malayalam, and Kannada.
-
Factors affecting ASR performance: A study using state of the art ASR models in Indic Languages
Empirical analysis of speaker and acoustic factors correlated with ASR word error rates across five Indic languages using zero-shot evaluation on multiple open-source models.