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

arXiv: https://arxiv.org/abs/2207.07118 · HTML: https://mlsanity.com/paper/2207.07118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts](https://mlsanity.com/paper/2101.03025/) (2020)
- [Predict Emoji Combination with Retrieval Strategy](https://mlsanity.com/paper/1908.07761/) (2019)
- [edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts](https://mlsanity.com/paper/2101.03916/) (2021)
- [VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation](https://mlsanity.com/paper/2112.12028/) (2021)
- [Language Detection Engine for Multilingual Texting on Mobile Devices](https://mlsanity.com/paper/2101.03963/) (2021)
- [LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic Algorithm driven Hyperparameter Tuning](https://mlsanity.com/paper/2202.02522/) (2022)
- [PrivPAS: A real time Privacy-Preserving AI System and applied ethics](https://mlsanity.com/paper/2202.02524/) (2022)

## Abstract (arXiv)

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.