迈向狗吠解码：利用人类语音处理进行自动狗吠分类
Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification
AI总结：
本文提出利用在人类语音上预训练的自监督语音表示模型进行狗吠分类，在狗识别、品种、性别和上下文四个任务上显著优于基线方法。
摘要：
与人类类似，动物广泛使用口头和非口头通信形式，包括大量音频信号。本文处理狗发声问题，并探索使用在人类语音上预训练的自监督语音表示模型，以解决在以人为中心的语音识别任务中找到对应关系的狗吠分类任务。我们具体处理四个任务：狗识别、品种识别、性别分类和上下文。我们表明，使用语音嵌入表示比简单分类基线有显著提升。此外，我们还发现，在大规模人类语音声学上预训练的模型可以在多个任务上提供额外的性能提升。
英文摘要：
Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks.
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。
