Contemporary conversational systems often present a significant limitation:
their responses lack the emotional depth and disfluent characteristic of human
interactions. This absence becomes particularly noticeable when users seek more
personalized and empathetic interactions. Consequently, this makes them seem
mechanical and less relatable to human users. Recognizing this gap, we embarked
on a journey to humanize machine communication, to ensure AI systems not only
comprehend but also resonate. To address this shortcoming, we have designed an
innovative speech synthesis pipeline. Within this framework, a cutting-edge
language model introduces both human-like emotion and disfluencies in a
zero-shot setting. These intricacies are seamlessly integrated into the
generated text by the language model during text generation, allowing the
system to mirror human speech patterns better, promoting more intuitive and
natural user interactions. These generated elements are then adeptly
transformed into corresponding speech patterns and emotive sounds using a
rule-based approach during the text-to-speech phase. Based on our experiments,
our novel system produces synthesized speech that's almost indistinguishable
from genuine human communication, making each interaction feel more personal
and authentic.

现代对话系统在情感深度和人类交互特征方面存在重大局限。为弥补这一差距，我们设计了一种创新的语音合成流程，通过零 - shot 设置下的先进语言模型将人类情感和不流畅的特质引入生成的文本，从而更好地模拟人类语音模式，提供更直观自然的用户交互，产生几乎无法与真实人类交流区分的合成语音，使每次交互更具个人化和真实性。