We present MADASR 2.0, a challenge at ASRU 2025 aimed at advancing multilingual and multidialectal automatic speech recognition (ASR) in low-resource Indian languages. Building on the 2023 edition, it introduces a subset of the RESPIN corpus, over 1200 hours of read speech across 8 languages and 33 dialects, with test sets including both read and spontaneous speech. The challenge comprises four tracks varying by training data size and external resource usage, and supports auxiliary tasks like language and dialect identification. We detail the dataset, tasks, baselines, and submissions and analyse trends across tracks and speech styles. Results highlight the continued difficulty of spontaneous ASR, the benefits of multitask and transfer learning, and effective strategies for building dialect-aware ASR systems. MADASR 2.0 offers a standardised benchmark to support future research on inclusive and scalable ASR for linguistically diverse populations.

Dialect identification (DID) addresses the challenge of recog-nizing regional variations within a language. The current deep learning approaches focus on audio-only, text-only, or multi-task setups combining automatic speech recognition (ASR) with DID. This work introduces a novel multimodal architecture that leverages speech and text features to enhance DID performance. Our method integrates ASR-generated speech representations with text embeddings derived from ASR hypotheses using a RoBERTa-based encoder. Additionally, we perform a layer-wise analysis of the IndicWav2Vec model to identify the layers most effective for extracting dialectal features. We evaluate our approach on a subset of the RESPIN dataset featuring eight Indian languages and 33 dialects. Experimental results show that our proposed multimodal DID system achieves an average DID accuracy of 79.81%, consistently outperforming baseline methods. This study is the first to analyse comprehensively DID in Indian languages, providing new insights into their dialectal diversity.

We introduce RESPIN-S1.0, the largest publicly available dialect-rich read-speech corpus for Indian languages, comprising more than 10,000 hours of validated audio across nine major languages: Bengali, Bhojpuri, Chhattisgarhi, Hindi, Kannada, Magahi, Maithili, Marathi, and Telugu. Indian languages exhibit high dialectal variation and are spoken by populations that remain digitally underserved. Existing speech corpora typically represent only standard dialects and lack domain and linguistic diversity. RESPIN-S1.0 addresses this limitation by collecting speech across more than 38 dialects and two high-impact domains: agriculture and finance. Text data were composed by native dialect speakers and validated through a pipeline combining automated and manual checks. Over 200,000 unique sentences were recorded through a crowdsourced mobile platform and categorised into clean, semi-noisy, and noisy subsets based on transcription quality, with the clean portion alone exceeding 10,000 hours. Along with audio and transcriptions, RESPIN provides dialect-aware phonetic lexicons, speaker metadata, and reproducible train, development, and test splits. To benchmark performance, we evaluate multiple ASR models, including TDNN-HMM, E-Branchformer, Whisper, and wav2vec2-based self-supervised models, and find that fine-tuning on RESPIN significantly improves recognition accuracy over pretrained baselines. A subset of RESPIN-S1.0 has already supported community challenges such as the SLT Code Hackathon 2022 and MADASR@ASRU 2023 and 2025, releasing more than 1,200 hours publicly. This resource supports research in dialectal ASR, language identification, and related speech technologies, establishing a comprehensive benchmark for inclusive, dialect-rich ASR in multilingual low-resource settings. Dataset: https://spiredatasets.ee.iisc.ac.in/respincorpus Code: https://github.com/labspire/respin_baselines.git