@article{gopinath-etal-2026-imasc,
    title = "{IM}a{SC}: A {M}alayalam Speech Corpus for High-Quality Text-to-Speech Synthesis",
    author = "Gopinath, Deepa P.  and
      D K, Thennal  and
      Nair, Vrinda V.  and
      S, Swaraj K.  and
      G, Sachin",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.465/",
    pages = "5864--5872",
    abstract = "Modern text-to-speech (TTS) systems use deep learning to synthesize speech increasingly approaching human quality, but they require a database of high-quality audio-text sentence pairs for training. Malayalam, the official language of the Indian state of Kerala and spoken by 35+ million people, is a low-resource language in terms of available corpora for TTS systems. In this paper, we present IMaSC, a Malayalam text and speech corpora containing 49 hours and 37 minutes of recorded speech. With 8 speakers and a total of 34,473 text-audio pairs, IMaSC is larger than every other publicly available alternative. We evaluated the database by using it to train TTS models for each speaker based on a modern deep learning architecture. With an average mean opinion score of 4.50, we find that the synthesized speech of our model is close to human quality."
}