# Classification of Spontaneous and Scripted Speech for Multilingual Audio

arXiv: https://arxiv.org/abs/2412.11896 · HTML: https://mlsanity.com/paper/2412.11896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Improved DeepFake Detection Using Whisper Features](https://mlsanity.com/paper/2306.01428/) (2023)
- [Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis](https://mlsanity.com/paper/2309.15656/) (2023)
- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization](https://mlsanity.com/paper/2311.05203/) (2023)
- [An Audio-Based Deep Learning Framework For BBC Television Programme Classification](https://mlsanity.com/paper/2104.01161/) (2021)

## Built on by (most cited first, 4 of 4)

- [SEAM: Shortcut-Aware Real-Time Detection of Scripted vs. Spontaneous Speech for Interview Guardrails](https://mlsanity.com/paper/2606.06837/) (2026)
- [Audio-Based Understanding of Audiobook Narration Appeal](https://mlsanity.com/paper/2607.02473/) (2026)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)
- [Evaluating Language Models in Realistic Conversational Contexts](https://mlsanity.com/paper/2608.26131/) (2026)

## Abstract (arXiv)

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.