LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech
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.
Cite
@article{arxiv.2207.07118,
title = {LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech},
author = {Harshvardhan Anand and Nansi Begam and Richa Verma and Sourav Ghosh and Harichandana B. S. S and Sumit Kumar},
journal= {arXiv preprint arXiv:2207.07118},
year = {2022}
}
Comments
Best Paper Award recipient at IEEE CONECCT 2022 in "Consumer Technology" track. Accepted at the 8th IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), July 8-10, 2022. Contains main paper and 4 additional pages of supplementary material