对比增强：语音技术中关键词识别的无监督学习方法
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology
AI总结：
本文提出CAB-KWS方法，将无监督对比学习与语音增强及压缩卷积架构相结合，在有限标注数据下提升关键词识别性能，并在Google Speech Commands V2数据集上验证了其有效性。
摘要：
本文针对语音技术中基础组成部分——关键词识别（KWS）在获取大量标注训练数据方面长期存在的挑战。由于难以获得大量正样本，且当关键词变化时收集新目标样本的过程十分费力，我们提出了一种结合无监督对比学习和独特的基于增强技术的新方法。我们的方法允许神经网络在无标注数据集上进行训练，从而有可能在标注数据集有限的情况下提升下游任务的性能。我们还提出，对于具有相同关键词的语音话语，即使语速或音量存在变化，也应采用相似的高层特征表示。为实现这一目标，我们提出了一种基于语音增强的无监督学习方法，利用瓶颈层特征与音频重构信息之间的相似性进行辅助训练。此外，我们提出了一种压缩卷积架构，以解决KWS任务中潜在的冗余和非信息性信息问题，使模型能够同时学习局部特征并关注长期信息。该方法在Google Speech Commands V2数据集上取得了优异性能。受近期手语识别和口语词检测进展的启发，我们的方法凸显了对比学习方法在KWS中的潜力以及基于样例查询的口语词检测策略的优势。所提出的CAB-KWS为KWS领域提供了新的视角，展示了减少数据收集工作量和提高系统鲁棒性的有效途径。
英文摘要：
This paper addresses the persistent challenge in Keyword Spotting (KWS), a fundamental component in speech technology, regarding the acquisition of substantial labeled data for training. Given the difficulty in obtaining large quantities of positive samples and the laborious process of collecting new target samples when the keyword changes, we introduce a novel approach combining unsupervised contrastive learning and a unique augmentation-based technique. Our method allows the neural network to train on unlabeled data sets, potentially improving performance in downstream tasks with limited labeled data sets. We also propose that similar high-level feature representations should be employed for speech utterances with the same keyword despite variations in speed or volume. To achieve this, we present a speech augmentation-based unsupervised learning method that utilizes the similarity between the bottleneck layer feature and the audio reconstructing information for auxiliary training. Furthermore, we propose a compressed convolutional architecture to address potential redundancy and non-informative information in KWS tasks, enabling the model to simultaneously learn local features and focus on long-term information. This method achieves strong performance on the Google Speech Commands V2 Dataset. Inspired by recent advancements in sign spotting and spoken term detection, our method underlines the potential of our contrastive learning approach in KWS and the advantages of Query-by-Example Spoken Term Detection strategies. The presented CAB-KWS provide new perspectives in the field of KWS, demonstrating effective ways to reduce data collection efforts and increase the system's robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
