Abstract
Recent advancement in deep learning encouraged developing large automatic speech recognition (ASR) models that achieve promising results while ignoring computational and memory constraints. However, deploying such models on low resource devices is impractical despite of their favorable performance. Existing approaches (pruning, distillation, layer skip etc.) transform the large models into smaller ones at the cost of significant performance degradation or require prolonged training of smaller models for better performance. To address these issues, we introduce an efficacious two-step representation learning based approach capable of producing several small sized models from a single large model ensuring considerably better performance in limited number of epochs. Comprehensive experimentation on ASR benchmarks reveals the efficacy of our approach, achieving three-fold training speed-up and up to 12.54% word error rate improvement.
Abstract (translated)
最近的深度学习进展鼓励开发出了一系列大规模自动语音识别（ASR）模型，这些模型在忽略计算和内存限制的情况下取得了令人鼓舞的结果。然而，在资源有限的设备上部署这样的大模型是不切实际的，尽管它们有良好的性能表现。现有的方法（如剪枝、蒸馏、跳过层等），虽然可以将大型模型转换为较小的模型，但会导致显著的性能下降或需要长时间训练小型模型以获得更好的性能。 为了应对这些问题，我们提出了一种有效的两步表示学习方法，可以从单个大规模模型中生成多个小规模模型，并确保在有限的训练周期内有相当不错的性能表现。我们在ASR基准测试上的全面实验表明了该方法的有效性，实现了三倍的训练速度提升，并且错误词率（WER）最多减少了12.54%。
URL
https://arxiv.org/abs/2505.16991
