# Sounding Like a Winner? Prosodic Differences in Post-Match Interviews

arXiv: https://arxiv.org/abs/2506.02283 · HTML: https://mlsanity.com/paper/2506.02283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 2 Jun 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets](https://mlsanity.com/paper/2406.10275/) (2024)
- [Speech-based emotion recognition with self-supervised models using attentive channel-wise correlations and label smoothing](https://mlsanity.com/paper/2211.01756/) (2022)
- [Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations](https://mlsanity.com/paper/2210.09513/) (2022)

## Built on by (most cited first, 3 of 3)

- [iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning](https://mlsanity.com/paper/2605.17179/) (2026)
- [The Role of Prosodic and Lexical Cues in Turn-Taking with Self-Supervised Speech Representations](https://mlsanity.com/paper/2601.13835/) (2026)
- [iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis](https://mlsanity.com/paper/2602.21464/) (2026)

## Abstract (arXiv)

This study examines the prosodic characteristics associated with winning and losing in post-match tennis interviews. Additionally, this research explores the potential to classify match outcomes solely based on post-match interview recordings using prosodic features and self-supervised learning (SSL) representations. By analyzing prosodic elements such as pitch and intensity, alongside SSL models like Wav2Vec 2.0 and HuBERT, the aim is to determine whether an athlete has won or lost their match. Traditional acoustic features and deep speech representations are extracted from the data, and machine learning classifiers are employed to distinguish between winning and losing players. Results indicate that SSL representations effectively differentiate between winning and losing outcomes, capturing subtle speech patterns linked to emotional states. At the same time, prosodic cues -- such as pitch variability -- remain strong indicators of victory.