CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation

Existing fine-grained intensity regulation methods rely on explicit control through predicted emotion probabilities. However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning. Especially when we attempt to mix multiple emotion intensities for specific phonemes, resulting in markedly reduced controllability and naturalness of the synthesis. To address this issue, we propose the CAScaded Explicit and Implicit coNtrol framework (CASEIN), which leverages accurate disentanglement of emotion manifolds from the reference speech to learn the implicit representation at a lower semantic level. This representation bridges the semantical gap between explicit probabilities and the synthesis model, reducing bias in learning. In experiments, our CASEIN surpasses existing methods in both controllability and naturalness. Notably, we are the first to achieve fine-grained control over the mixed intensity of multiple emotions.

AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience

We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.