Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation
This addresses the need for more personalized and empathetic AI interactions for users of conversational systems, though it appears incremental as it builds on existing synthesis methods.
The paper tackles the problem of mechanical-sounding conversational systems by developing a speech synthesis pipeline that generates human-like emotion and disfluencies in a zero-shot setting, resulting in synthesized speech that is almost indistinguishable from genuine human communication.
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.