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

In this work, several methods have been proposed towards improving the performance of dialectal automatic speech recognition (ASR). A novel encoder architecture has been introduced that is suited for multi-dialect ASR training. Further, we propose Multi-Task Self-Supervised learning (SSL) fine-tuning using CTC and dialect identification. Additionally, the use of different language models (LM) to improve the performance of dialectal ASR has been investigated. Around 800 hours of Bengali and Bhojpuri data, released as a part of the MADASR ASRU challenge have been used to train these models. The work shows that the proposed multi-encoder ASR observes a relative reduction of 7.5% and 9% in WER in Bhojpuri and Bengali, respectively. Additionally, we also observe a 1-2% WER reduction in fine-tuning SSL, further improving performance in these languages. Moreover, we observe advantages in using dialect-specific LM decoding based on predicted dialect.

Out-of-Vocabulary (OOV) detection and recovery is an important aspect of reducing Word Error Rate (WER) in Automatic Speech Recognition (ASR). In this paper, we evaluate the effect of OOV detection and recovery for a low-resource language on WER. We use a small seed corpus of continuous speech and improve the vocabulary by incorporating the detected OOV words. We use a syllable-model to learn OOV words and augment the word-model with these words leading to improved recognition. Our research investigates the effect on OOV detection and recovery after adding missing syllable sounds in the syllable model using a Text-to-Speech (TTS) system. Our experiments are conducted using 5 hours of continuous speech Kannada corpus. We use an already available Festival TTS for Hindi to generate Kannada speech. Our initial experiments report an improvement in OOV detection due to addition of missing syllable sounds using a cross-lingual TTS system.

In today's age of information technology, there is an increasing concern for the privacy of an individual's records stored on computers. The Internet and security vulnerabilities provide an opportunity for hackers to misuse information. The level of an individual's trust for an organization can be ensured or influenced by the organization's privacy policy. Privacy has become a concern only recently and hence, creating well documented and comprehensive organizational privacy policies still remains a challenge. The paper presents results on a special privacy policy modeling language processor (PPMLP) based on service oriented architecture (SOA) for an organization to model the structure and contents of private policy they want through a meta type of privacy policy specifications.