4 accepted papers
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 archite…
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 languag…
The Lightweight, Multi-speaker, Multi-lingual Indic Text-to-Speech (LIMMITS’23) challenge is organized as part of the ICASSP 2023 signal processing grand challenge. LIMMITS’23 aims at the development of a lightweight, multi-speaker, multi-lingual Text to Speech (TTS) model using datasets in Marathi,…
Unlike phoneme sequences, movements of speech articulators (lips, tongue, jaw, velum) and the resultant acoustic signal are known to encode not only the linguistic message but also carry para-linguistic information. While several works exist for estimating articulatory movement from acoustic signals…