针对多人对话自然度，提出双通道自动评估模型。
Conversational Speech Naturalness Predictor
- 设计双通道模型，融合多方语音特征捕捉对话流畅性。
- 在域内与域外测试中，相关性显著高于现有方法。
- 适合对话系统研发者用于优化人机交互自然度。
对话自然度评估对打造类人语音代理至关重要。然而，现有语音自然度评估模型多针对单说话人语句设计，难以捕捉对话层面的自然度特征。本文提出一种面向双说话人、多轮对话的自动自然度预测框架。基于带有真人评分的对话录音，我们发现现有自然度评估器与对话自然度的相关性较低，甚至为负值。随后提出一种双通道自然度评估器，探索多种预训练编码器并结合数据增强策略。所提模型在域内与域外条件下均显著提升与人类判断的相关性。
原文摘要 · Abstract (English)
Evaluation of conversational naturalness is essential for developing human-like speech agents. However, existing speech naturalness predictors are often designed to assess utterances from a single speaker, failing to capture conversation-level naturalness qualities. In this paper, we present a framework for an automatic naturalness predictor for two-speaker, multi-turn conversations. We first show that existing naturalness estimators have low, or sometimes even negative, correlations with conversational naturalness, based on conversational recordings annotated with human ratings. We then propose a dual-channel naturalness estimator, in which we investigate multiple pre-trained encoders with data augmentation. Our proposed model achieves substantially higher correlation with human judgments compared to existing naturalness predictors for both in-domain and out-of-domain conditions.
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