NeuralMultiling: A Novel Neural Architecture Search for Smartphone based Multilingual Speaker Verification
<|reference_start|>NeuralMultiling: A Novel Neural Architecture Search for Smartphone based Multilingual Speaker Verification: Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of the language. Instead of exploring the design space manually, we propose a neural architecture search for multilingual speaker verification suitable for mobile devices, called \textbf{NeuralMultiling}. First, our algorithm searches for an optimal operational combination of neural cells with different architectures for normal cells and reduction cells and then derives a CNN model by stacking neural cells. Using the derived architecture, we performed two different studies:1) language agnostic condition and 2) interoperability between languages and devices on the publicly available Multilingual Audio-Visual Smartphone (MAVS) dataset. The experimental results suggest that the derived architecture significantly outperforms the existing Autospeech method by a 5-6\% reduction in the Equal Error Rate (EER) with fewer model parameters.<|reference_end|>
@article{pn2024neuralmultiling:,
    title={NeuralMultiling: A Novel Neural Architecture Search for Smartphone based
  Multilingual Speaker Verification},
    author={Aravinda Reddy PN, Raghavendra Ramachandra, K. Sreenivasa Rao, Pabitra
  Mitra},
    journal={arXiv preprint arXiv:2408.04362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04362},
    primaryClass={cs.SD eess.AS}
}