Speech conveys more information than just text, as the same word can be
uttered in various voices to convey diverse information. Compared to
traditional text-to-speech (TTS) methods relying on speech prompts (reference
speech) for voice variability, using text prompts (descriptions) is more
user-friendly since speech prompts can be hard to find or may not exist at all.
TTS approaches based on the text prompt face two challenges: 1) the one-to-many
problem, where not all details about voice variability can be described in the
text prompt, and 2) the limited availability of text prompt datasets, where
vendors and large cost of data labeling are required to write text prompt for
speech. In this work, we introduce PromptTTS 2 to address these challenges with
a variation network to provide variability information of voice not captured by
text prompts, and a prompt generation pipeline to utilize the large language
models (LLM) to compose high quality text prompts. Specifically, the variation
network predicts the representation extracted from the reference speech (which
contains full information about voice) based on the text prompt representation.
For the prompt generation pipeline, it generates text prompts for speech with a
speech understanding model to recognize voice attributes (e.g., gender, speed)
from speech and a large language model to formulate text prompt based on the
recognition results. Experiments on a large-scale (44K hours) speech dataset
demonstrate that compared to the previous works, PromptTTS 2 generates voices
more consistent with text prompts and supports the sampling of diverse voice
variability, thereby offering users more choices on voice generation.
Additionally, the prompt generation pipeline produces high-quality prompts,
eliminating the large labeling cost. The demo page of PromptTTS 2 is available
online\footnote{this https URL}.

基于 PromptTTS 2，本研究通过利用变异网络和生成提示管道来解决基于文本提示的文本到语音 (TTS) 方法的挑战，提供声音的多样性信息并生成高质量的文本提示，从而产生与文本提示一致且具有多样性的声音。