标题： HuBERT 多分辨率探索 显示英文标题

标题： Exploration on HuBERT with Multiple Resolutions

摘要： Hubert（隐藏单元 BERT）是一种在语音处理中广泛使用的自监督学习 (SSL) 模型。然而，我们认为其隐藏表示的固定 20 毫秒分辨率对于各种语音处理任务可能并非最佳，因为这些任务的属性（例如说话者特征和语义）基于不同的时间尺度。为了解决这一局限性，我们建议在下游任务中使用多分辨率的 Hubert 表示。我们探索了两种方法，即并行方法和分层方法，用于整合具有不同分辨率的 Hubert 特征。通过实验，我们证明了多分辨率 Hubert 模型的表现优于原始模型。这突显了在像 Hubert 这样的 SSL 模型中利用多分辨率以从语音信号中捕获多样化信息的潜力。 显示英文摘要

摘要： Hidden-unit BERT (HuBERT) is a widely-used self-supervised learning (SSL) model in speech processing. However, we argue that its fixed 20ms resolution for hidden representations would not be optimal for various speech-processing tasks since their attributes (e.g., speaker characteristics and semantics) are based on different time scales. To address this limitation, we propose utilizing HuBERT representations at multiple resolutions for downstream tasks. We explore two approaches, namely the parallel and hierarchical approaches, for integrating HuBERT features with different resolutions. Through experiments, we demonstrate that HuBERT with multiple resolutions outperforms the original model. This highlights the potential of utilizing multiple resolutions in SSL models like HuBERT to capture diverse information from speech signals.