The Open ASR Leaderboard Adds Its First Global South Language

Design of the collection Dataset composition Speaker coverage Metadata fields Collection and quality control Regional variation Orthographic variation in Hindi Getting evaluated What comes next Voice Arena and Hugging Face partner to launch open ASR evaluation for Hindi and Indian English Benchmarks decide what gets built. A model that scores well on the Open ASR Leaderboard gets adopted and iterated on, while capabilities the leaderboard does not measure tend not to improve.

Much of the recent work on the leaderboard has gone into making the evaluation metrics more trustworthy: All of that makes one number (WER) harder to game. It is still one number. A long line of work has shown that ASR error rates are not evenly distributed across the people using them. Racial disparities in automated speech recognition found commercial systems roughly twice as bad for Black speakers as for white speakers, and Quantifying Bias in Automatic Speech Recognition found further differences by gender, age and accent. None of that is visible on a leaderboard, and not because the leaderboard is hiding it. The test sets it runs on record what was said and almost nothing about who said it. To address this gap, we introduce two evaluation sets to the Open ASR Leaderboard: Monsoon en-IN and Monsoon hi-IN. Hindi, spoken by more than half a billion people, is the first Indic language on a multilingual tab that currently covers only European languages. Each set is released as a public split, available for self-scoring, and a private split withheld to limit benchmark-specific optimisation. The four splits are speaker-disjoint, comprising 4,888 speakers, with 12 speaker attributes recorded for each. A test set can only expose a failure mode it varies along. Most benchmarks are built from whatever audio was readily available. Monsoon was built to vary along nine axes: geography, age, gender, vocabulary, devices, acoustic environments, speech type, speech rate, and the existence of multiple valid transcripts for the same audio. Each is a way an aggregate WER can be right on average and wrong for a particular population. Four splits, two languages, collected through one pipeline.