LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech

TL;DR

This paper introduces LIP, a lightweight preprocessor that improves text readability for TTS systems by handling emojis, punctuation, PII, and offensive words, enabling real-time, privacy-aware speech synthesis.

Contribution

The paper presents the first lightweight, real-time preprocessor for TTS that effectively manages emojis, punctuation, and PII, enhancing speech synthesis quality.

Findings

76.5% user preference for LIP-enabled TTS over standard TTS

Memory footprint of only 3.55 MB for the preprocessor

Inference time of 4 ms for 50-character text

Abstract

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.