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arXiv · 2207.07118

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

Harshvardhan Anand·Nansi Begam·Richa Verma·Sourav Ghosh·Harichandana B. S. S·Sumit Kumar
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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.

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Harshvardhan Anand, Nansi Begam, Richa Verma, Sourav Ghosh, Harichandana B. S. S, Sumit Kumar. 2022-07-11. LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech. https://doi.org/10.1109/conecct55679.2022.9865708

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