Humane Speech Synthesis through Zero-Shot Emotion and Disfluency
  Generation
Rohan Chaudhury Mihir Godbole Aakash Garg Jinsil Hwaryoung Seo
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.