A Novel Loss Function and Training Strategy for Noise-Robust Keyword Spotting
Iván López‐Espejo, Zheng‐Hua Tan, Jesper Jensen
Abstract
The development of keyword spotting (KWS) systems that are accurate in noisy conditions remains a challenge. Towards this goal, in this paper we propose a novel training strategy relying on multi-condition training for noise-robust KWS. By this strategy, we think of the state-of-the-art KWS models as the composition of a keyword embedding extractor and a linear classifier that are successively trained. To train the keyword embedding extractor, we also propose a new (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N,2</sub> +1)-pair loss function extending the concept behind related loss functions like triplet and N-pair losses to reach larger inter-class and smaller intra-class variation. Experimental results on a noisy version of the Google Speech Commands Dataset show that our proposal achieves around 12% KWS accuracy relative improvement with respect to standard end-to-end multi-condition training when speech is distorted by unseen noises. This performance improvement is achieved without increasing the computational complexity of the KWS model.