Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention. In this paper, we propose SVSNet, the first end-to-end neural network model to assess the speaker voice similarity between converted speech and natural speech for voice conversion tasks. Unlike most neural evaluation metrics that use hand-crafted features, SVSNet directly takes the raw waveform as input to more completely utilize speech information for prediction. SVSNet consists of encoder, co-attention, distance calculation, and prediction modules and is trained in an end-to-end manner. The experimental results on the Voice Conversion Challenge 2018 and 2020 (VCC2018 and VCC2020) datasets show that SVSNet outperforms well-known baseline systems in the assessment of speaker similarity at the utterance and system levels.

翻译：本文建议SVSNet是第一个终端到终端神经网络模型,用于评估语音转换任务的语音转换和自然语音转换功能之间的声音相似性。与大多数使用手工制作功能的神经评价指标不同的是,SVSNet直接将原始波状作为投入,以便更全面地利用语音信息进行预测。SVSNet由编码器、共同关注、远程计算和预测模块组成,并以端到端方式进行培训。2018年和2020年语音转换挑战(VCC2018和VCC202020)的实验结果显示,SVSNet在评估语音和系统层面的类似性时,超越了众所周知的基线系统。