English Earnings Calls Corpus With Global Accents

Functioning as a 119-hour corpus of English-language earnings calls collected from global companies. It serves as a benchmark for automatic speech recognition models on real-world accented speech. The author is anton-l and it was last updated in June 2022.

Use Cases

• Benchmark ASR model performance on real-world accented speech from global companies
• Train speech recognition systems on the 119-hour corpus of earnings call audio
• Analyze acoustic characteristics of English speech from diverse corporate speakers

Strengths

• 119 hours of audio data provides substantial material for model training
• Focus on real-world accented speech from global companies offers practical relevance
• Explicitly designed as a benchmark for industrial and academic ASR models

Limitations

• Limited to earnings calls, which may not represent general conversational speech
• No information on the number of unique speakers or companies included
• Potential class imbalance if certain accents or regions are overrepresented

Provenance

Source

huggingface

Collection Method

Collected earnings calls from global companies.

Freshness

Last updated in June 2022.

Geography

Global