Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approaches have utilized self-supervised models pre-trained on human speech for feature extraction, capitalizing on their ability to learn a signal's intrinsic structure independently of its acoustic domain. However, the utility of such foundation models remains unclear for marmoset call analysis in terms of multi-class classification, bandwidth, and pre-training domain. This study assesses feature representations derived from speech and general audio domains, across pre-training bandwidths of 4, 8, and 16 kHz for marmoset call-type and caller classification tasks. Results show that models with higher bandwidth improve performance, and pre-training on speech or general audio yields comparable results, improving over a spectral baseline.

翻译：狨猴通过叫声编码重要信息，为神经生物学家研究人类语音交流的进化起源提供了替代模型。传统方法采用基于信号处理的特征进行分析，而近期研究则利用在人类语音上预训练的自监督模型进行特征提取，这类模型能够独立于声学领域学习信号的内在结构。然而，此类基础模型在狨猴叫声分析中的多类别分类、带宽及预训练领域等方面的效用尚不明确。本研究评估了来自语音与通用音频领域的特征表示，在4、8和16 kHz三种预训练带宽下，对狨猴叫声类型及个体识别任务的性能。结果表明，更高带宽的模型能提升分类性能，且基于语音或通用音频的预训练均能取得可比的结果，其表现优于谱特征基线方法。