IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages
Abstract
AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear whether these models genuinely utilise such context or rely on parametric knowledge learned during pretraining. Existing benchmarks cannot answer this question because they evaluate transcription under fixed prompting conditions and rarely include explicit contextual inputs. We introduce IndicContextEval, a 56-hour multilingual benchmark of natural speech from 555 speakers across 8 Indian languages and 23 professional domains. We design a 7-level prompting framework that progressively introduces contextual signals, including metadata, natural-language descriptions, entity lists in English and native script, and adversarial prompts with incorrect entities. Evaluating five models reveals substantial differences in context utilisation behaviour, highlighting the need for explicit evaluation of contextual grounding in AudioLLMs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
IndicContextEval introduces a rigorous, multilingual benchmark that reveals critical gaps in how AudioLLMs utilize contextual information, demonstrating that while some models effectively leverage native-script entity biasing, others suffer from blind reliance or contextual blindness, thereby establishing a new standard for evaluating contextual grounding in speech recognition systems.
Methodology
The paper proposes a novel evaluation framework, IndicContextEval, designed to probe the contextual grounding capabilities of Audio Large Language Models (AudioLLMs). The core methodological contribution is the design of a 7-level prompting taxonomy (L0-L6) that systematically varies the type and quality of textual context provided to the model (from no context to adversarial incorrect entities). This controlled experimental design allows for the isolation of specific contextual signals (metadata, natural language descriptions, entity lists) and the measurement of their impact on transcription accuracy (WER) and entity recognition (NEER). The approach is rigorous in its control of variables, aiming to distinguish between parametric memorization and genuine contextual utilization.
Results & evaluation
The authors evaluate five leading AudioLLMs (GPT-4o Transcribe, Gemini 3 Flash, Sarvam Audio, Gemma-3N, and a standalone IndicConformer baseline) on a newly collected dataset of 56 hours of natural speech across 8 Indian languages and 23 professional domains. The results are insightful, revealing significant disparities in how models handle context. For instance, GPT-4o Transcribe shows robust contextual reasoning, while Gemma-3N exhibits "blind reliance" on entity prompts, even when they are adversarial. The finding that natural-language descriptions often outperform structured metadata, and that native-script entity biasing yields the largest gains, provides concrete empirical evidence for the field. The use of NEER as a primary metric for entity biasing is well-chosen and adds depth to the standard WER evaluation.
Reproducibility
The paper provides a clear description of the dataset creation process, including speaker demographics, recording styles (read vs. extempore), and quality control measures. The prompt taxonomy is explicitly defined, allowing other researchers to replicate the evaluation protocol. The code and benchmark data are made publicly available via GitHub, which significantly enhances reproducibility. The inclusion of specific model versions and the detailed breakdown of results by language and context level further support reproducibility efforts.
Limitations
The dataset, while diverse, is limited to 8 Indian languages and 23 domains, which may not capture the full spectrum of global linguistic diversity or domain-specific challenges. The reliance on commercial models (GPT-4o, Gemini) limits the ability to fully inspect internal mechanisms, although the black-box evaluation is appropriate for the benchmark's goals. The "adversarial" prompts in L6 are limited to incorrect domain entities; more sophisticated adversarial attacks (e.g., semantically similar but incorrect entities) could provide deeper insights into model robustness. Additionally, the dataset size (56 hours) is relatively small compared to large-scale ASR benchmarks, which may limit the statistical power of some analyses, particularly for lower-resource languages within the set.
Broader impact
This work has significant implications for the development and deployment of multilingual AudioLLMs, particularly in low-resource and high-context domains like healthcare, legal, and technical support in India. By highlighting the risks of blind reliance on context or failure to utilize it, the benchmark encourages the development of more robust and interpretable models. It also underscores the importance of native-script support and the challenges of cross-lingual entity biasing. The public release of the benchmark will facilitate fairer comparisons and drive progress in contextual ASR for Indic languages. IndicContextEval introduces a rigorous, multilingual benchmark that reveals critical gaps in how AudioLLMs utilize contextual information, demonstrating that while some models effectively leverage native-script entity biasing, others suffer from blind reliance or contextual blindness, thereby establishing a new standard for evaluating contextual grounding in speech recognition systems.
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