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

TL;DR

This paper investigates whether prosodic speech features and self-supervised learning representations can accurately classify tennis match outcomes from post-match interviews, revealing subtle emotional and vocal cues associated with winning or losing.

Contribution

It introduces the use of SSL speech models combined with prosodic features to classify match results, demonstrating the effectiveness of these methods in sports outcome prediction.

Findings

SSL representations effectively differentiate winners from losers

Prosodic cues like pitch variability are strong indicators of victory

Combining prosodic features with SSL improves classification accuracy

Abstract

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.