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EchoNet++: A multilingual soccer match audio commentary dataset.

Fahad Majeed1, Maria Nazir2, Marco Agus3

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. fama44316@hbku.edu.qa.

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Summary

This study introduces an audio analysis pipeline for soccer broadcasts, improving speech recognition and translation. The system offers scalable, language-agnostic audio understanding for professional sports media.

Keywords:
Audio denoising and segmentationAutomatic speech recognitionMultilingual audio processingSoccer broadcast commentary analysisVoice activity detection

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Area of Science:

  • * Multimodal signal processing
  • * Computational linguistics
  • * Sports analytics

Background:

  • * Professional soccer broadcasts present complex audio environments with multiple overlapping speech and non-speech signals.
  • * Existing audio analysis methods often struggle with multilingual content and diverse acoustic conditions.
  • * Automatic Speech Recognition (ASR) and translation are crucial for content analysis but require robust pipelines.

Purpose of the Study:

  • * To develop and evaluate a comprehensive, reproducible audio analysis pipeline for professional soccer match broadcasts.
  • * To systematically analyze the impact of individual pipeline components (preprocessing, segmentation, transcription, translation) on overall performance.
  • * To enable scalable, language-agnostic audio understanding for broadcast content.

Main Methods:

  • * A unified pipeline for audio extraction, denoising (Demucs), speech segmentation (Silero VAD), and commentator/spectator stream categorization.
  • * Frequency domain transformation (FFT) and bandpass filtering (300 Hz-7 kHz) for speech signal isolation.
  • * Multilingual transcription using various Automatic Speech Recognition (ASR) models (Whisper variants, Insanely Fast Whisper) and English translation.

Main Results:

  • * Achieved low word error rates and accurate language detection across diverse acoustic conditions in full-match videos.
  • * Demonstrated consistent speech segmentation and reliable categorization of commentator/spectator audio streams.
  • * Successfully processed multilingual audio, producing structured JSON outputs with transcripts, translations, and metadata.

Conclusions:

  • * The developed pipeline provides a robust framework for automatic audio processing in sports broadcasts.
  • * The systematic component analysis offers insights into optimizing ASR and translation performance in real-world scenarios.
  • * Public release of the dataset and pipeline will facilitate reproducible research and downstream applications like summarization and analytics.