论文标题
迈向低成本的端到端口语理解
Toward Low-Cost End-to-End Spoken Language Understanding
论文作者
论文摘要
口语理解的最新进展受益于接受大型语音语料库训练的自制模型。对于法国人来说，Lebenchmark项目使此类模型可用，并在包括口语理解在内的几项任务上取得了令人印象深刻的进步。这些进步在计算时间和能耗方面具有不可忽略的成本。在本文中，我们比较了一些学习策略，试图降低这种成本，同时保持竞争性能。同时，我们提出了一项广泛的分析，我们在训练时间和电能消耗方面衡量模型的成本，希望促进全面的评估程序。实验是在FSC和Media Corpora上进行的，并表明可以在保持最先进的性能和使用SSL模型的同时降低学习成本。
Recent advances in spoken language understanding benefited from Self-Supervised models trained on large speech corpora. For French, the LeBenchmark project has made such models available and has led to impressive progress on several tasks including spoken language understanding. These advances have a non-negligible cost in terms of computation time and energy consumption. In this paper, we compare several learning strategies trying to reduce such cost while keeping competitive performance. At the same time we propose an extensive analysis where we measure the cost of our models in terms of training time and electric energy consumption, hopefully promoting a comprehensive evaluation procedure. The experiments are performed on the FSC and MEDIA corpora, and show that it is possible to reduce the learning cost while maintaining state-of-the-art performance and using SSL models.
