A Context-Based Numerical Format Prediction for a Text-To-Speech System

TL;DR

This paper introduces a context-based numerical format classifier for TTS systems, significantly improving the accuracy of numeric text synthesis by classifying six types of numeric contexts using machine learning techniques.

Contribution

It presents a novel feature extraction method and classifier ensemble that enhances numeric format prediction accuracy in TTS systems, outperforming existing techniques.

Findings

Classification accuracy improved by 30% to 37%.

Enhanced intelligibility of synthesized speech.

Effective use of support vector machines and other classifiers.

Abstract

Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using the proposed context-based feature extraction technique, which is focused on extracting keywords, punctuation marks, and symbols as the features of the numbers. Support Vector Machine, K-Nearest Neighbors Linear Discriminant Analysis, and Decision Tree were used as classifiers. We have used the 10-fold cross-validation technique to determine the classification accuracy in terms of recall and precision. It can be found that the proposed solution is better than the existing feature extraction technique with improvement to the classification accuracy by 30% to 37%. The use of the number format classification can increase the intelligibility of the TTS systems.