Classification of Spontaneous and Scripted Speech for Multilingual Audio

TL;DR

This study develops and evaluates multilingual speech classifiers to distinguish scripted from spontaneous speech, demonstrating that transformer models outperform traditional methods across diverse languages and datasets.

Contribution

It introduces a comprehensive evaluation of models for speech style classification across multiple languages and domains, highlighting the superiority of transformer-based approaches.

Findings

Transformer models outperform traditional features in accuracy.

Models generalize well across different languages.

Transformer models achieve state-of-the-art results.

Abstract

Distinguishing scripted from spontaneous speech is an essential tool for better understanding how speech styles influence speech processing research. It can also improve recommendation systems and discovery experiences for media users through better segmentation of large recorded speech catalogues. This paper addresses the challenge of building a classifier that generalises well across different formats and languages. We systematically evaluate models ranging from traditional, handcrafted acoustic and prosodic features to advanced audio transformers, utilising a large, multilingual proprietary podcast dataset for training and validation. We break down the performance of each model across 11 language groups to evaluate cross-lingual biases. Our experimental analysis extends to publicly available datasets to assess the models' generalisability to non-podcast domains. Our results indicate that transformer-based models consistently outperform traditional feature-based techniques, achieving state-of-the-art performance in distinguishing between scripted and spontaneous speech across various languages.