NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks
<|reference_start|>NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks: Self-supervised learning has been proved to benefit a wide range of speech processing tasks, such as speech recognition/translation, speaker verification and diarization, etc. However, most of current approaches are computationally expensive. In this paper, we propose a simplified and more efficient self-supervised learning framework termed as NeMo Encoder for Speech Tasks (NEST). Specifically, we adopt the FastConformer architecture with 8x sub-sampling rate, which is faster than Transformer or Conformer architectures. Instead of clustering-based quantization, we use fixed random projection for its simplicity and effectiveness. We also implement a generalized noisy speech augmentation that teaches the model to disentangle the main speaker from noise or other speakers. Experiments show that \model improves over existing self-supervised models and achieves new state-of-the-art performance on a variety of speech processing tasks, such as speech recognition/translation, speaker diarization, spoken language understanding, etc. Code and checkpoints will be publicly available via NVIDIA NeMo framework.<|reference_end|>
@article{huang2024nest:,
    title={NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech
  Processing Tasks},
    author={He Huang, Taejin Park, Kunal Dhawan, Ivan Medennikov, Krishna C.
  Puvvada, Nithin Rao Koluguri, Weiqing Wang, Jagadeesh Balam, Boris Ginsburg},
    journal={arXiv preprint arXiv:2408.13106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13106},
    primaryClass={cs.SD eess.AS}
}