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TL;DR
The Open ASR Leaderboard on Hugging Face has introduced Hindi and Indian English evaluation sets, making Hindi the first Indic and Global South language included. This development broadens the scope of speech recognition benchmarks to better reflect diverse populations and language varieties.
The Open ASR Leaderboard on Hugging Face has added two new evaluation sets, Monsoon en-IN for Indian English and Monsoon hi-IN for Hindi, making Hindi the first Indic and first Global South language featured on the platform. This marks a significant step toward diversifying speech recognition benchmarks and addressing biases in ASR systems, which have historically focused on European languages. For more details, see the original analysis.
These new sets include a total of 4,888 speakers across four splits—public and private—covering both Indian English and Hindi. The datasets are composed of unscripted, spontaneous conversations recorded from speakers across hundreds of districts, using their own devices and in varied environments, thereby capturing a wide range of acoustic and demographic diversity. For a detailed overview, see the original analysis. This development is discussed in detail in the original analysis.
Each clip records 12 speaker attributes, including age, gender, occupation, education, income, and location, enabling detailed analysis of model performance across different populations. The Indian English sets include roughly 11 hours of audio, while the Hindi sets contain about 5 hours. The datasets are designed to reflect real-world variability, such as different devices, environments, and speech registers, with the aim of improving the fairness and robustness of ASR models.
Impact of Including Hindi and Indian English on Speech Recognition Benchmarks
Adding Hindi, spoken by over half a billion people, introduces a major market and linguistic group to the benchmarking landscape. It addresses a longstanding gap in speech recognition research, which has mainly focused on European languages, and signals a move toward more inclusive AI development.
This development allows researchers and developers to evaluate whether their models perform equitably across diverse populations, considering factors like region, age, gender, and device type. The inclusion of detailed speaker metadata aims to uncover biases and disparities, fostering the creation of more fair and accurate ASR systems. Ultimately, this expansion can influence industry standards, encouraging broader language coverage and more equitable AI solutions for underserved communities.
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Background on Bias in Speech Recognition and Dataset Development
Historically, speech recognition systems have demonstrated disparities in performance across different demographic groups, often performing worse for speakers of non-European languages, accents, or lower socioeconomic backgrounds. Research such as ‘Racial Disparities in Automated Speech Recognition’ and ‘Quantifying Bias in Automatic Speech Recognition’ has documented these issues, highlighting the need for more representative datasets.
The Hugging Face Open ASR Leaderboard has served as a key benchmark for evaluating model accuracy primarily on European languages, with little focus on the Global South or Indic languages. Recent efforts have aimed to improve measurement robustness, including private test splits and improved normalisation techniques, but until now, no benchmark has explicitly included languages like Hindi or Indian English.
The Monsoon datasets were developed to address this gap, with input from contributors across diverse regions and environments, capturing the variability inherent in real-world speech. This approach aims to provide a more comprehensive evaluation framework that can guide the development of fairer, more inclusive ASR models.
“Including Hindi and Indian English on the leaderboard is a significant step toward more inclusive AI, reflecting the linguistic diversity of over half a billion speakers.”
— Thorsten Meyer, Lead Developer at Hugging Face
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Unanswered Questions About Model Performance and Dataset Stability
It is not yet clear how existing top-performing models on the current leaderboard perform on these new datasets, given their limited size—especially the Hindi set, which contains only 1.33 hours of audio. The stability of rankings and whether models will generalize well across such diverse and variable data remains to be seen.
Additionally, the effectiveness of the lattice approach for Hindi, which allows multiple valid spellings, compared to traditional normalisation techniques, has not been empirically demonstrated. Whether current normalisation methods can handle the linguistic variability in Hindi speech is still under investigation.
Finally, it is unclear if and how leaderboard participants will disaggregate results by the 12 recorded speaker attributes, which could reveal biases or disparities in model performance across different demographic groups.
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Next Steps for Benchmark Expansion and Model Evaluation
Researchers and developers are expected to begin testing their models on the new datasets, with initial results likely to be published in upcoming papers or leaderboard submissions. The private splits will be used to evaluate model generalization and reduce overfitting, with the goal of establishing more reliable benchmarks.
Further research will focus on increasing dataset size, especially for Hindi, and refining normalisation and scoring techniques to better handle linguistic variability. The community may also explore disaggregated results based on speaker attributes to identify and address biases.
In the longer term, expanding to include more languages from the Global South and developing standardized benchmarks for underrepresented languages will likely be prioritized, fostering more equitable AI development across diverse linguistic communities.
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Key Questions
Why is the inclusion of Hindi on the Open ASR Leaderboard important?
It introduces the first Indic and Global South language to a major speech recognition benchmark, addressing a significant gap and encouraging the development of more inclusive, equitable ASR systems for over half a billion speakers.
How might this development impact speech recognition technology?
It enables evaluation of models across diverse languages and demographics, helping identify biases and improve accuracy for underrepresented populations, ultimately leading to fairer and more reliable ASR systems.
What are the limitations of the current datasets?
The Hindi dataset is relatively small, with only about 1.33 hours of audio, which may limit the stability of model rankings and generalization. More data and further testing are needed to confirm model performance across diverse speakers and environments.
Will future datasets include more Global South languages?
While not explicitly confirmed, the inclusion of Hindi signals a move toward broader language diversity, and the community is likely to prioritize expanding benchmarks to other underrepresented languages in the future.
How will speaker metadata be used in evaluating models?
Researchers can analyze model performance across different demographic attributes such as age, gender, and region, helping to identify and mitigate biases in speech recognition systems.
Primary source: Hugging Face · via ThorstenMeyerAI.com
