Existing Text-to-Speech (TTS) systems need to read messages from the email
which may have Personal Identifiable Information (PII) to text messages that
can have a streak of emojis and punctuation. 92% of the world's online
population use emoji with more than 10 billion emojis sent everyday. Lack of
preprocessor leads to messages being read as-is including punctuation and
infographics like emoticons. This problem worsens if there is a continuous
sequence of punctuation/emojis that are quite common in real-world
communications like messaging, Social Networking Site (SNS) interactions, etc.
In this work, we aim to introduce a lightweight intelligent preprocessor (LIP)
that can enhance the readability of a message before being passed downstream to
existing TTS systems. We propose multiple sub-modules including: expanding
contraction, censoring swear words, and masking of PII, as part of our
preprocessor to enhance the readability of text. With a memory footprint of
only 3.55 MB and inference time of 4 ms for up to 50-character text, our
solution is suitable for real-time deployment. This work being the first of its
kind, we try to benchmark with an open independent survey, the result of which
shows 76.5% preference towards LIP enabled TTS engine as compared to standard
TTS.

本文介绍了一种轻量级的智能预处理器 LIP，旨在解决现有 TTS 系统无法识别标点符号和表情符号等消息中的信息，提出多个子模块，包括展开缩写、审查脏话、掩盖 PII 等，以提高文本的可读性。通过一项独立的调查，结果表明，相比标准 TTS，启用了 LIP 的 TTS 引擎的偏好度为 76.5%。