LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech
This addresses readability issues in TTS for messaging and social media users, but it is incremental as it builds on existing TTS systems with preprocessing modules.
The paper tackles the problem of text-to-speech (TTS) systems reading messages with personal identifiable information, emojis, and punctuation as-is, which reduces readability, by introducing a lightweight intelligent preprocessor (LIP) that enhances message readability before TTS processing, achieving 76.5% user preference in a survey.
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.