Audio Recordings of Spoken Digits for Speech Recognition

AudioMNIST is a research baseline dataset for audio and speech tasks. It contains spoken digit recordings, with tags indicating it includes audio modality and text modality, and is categorized as having between 10,000 and 100,000 samples.

Use Cases

• Train a speech recognition model to classify spoken digits using the audio modality data.
• Analyze audio features for digit pronunciation across different speakers, leveraging the text modality tags for alignment.
• Benchmark audio classification algorithms on a dataset with a scale of 10K to 100K samples.

Strengths

• Dataset size is categorized between 10,000 and 100,000 samples, providing a substantial volume for model training.
• Includes multiple data modalities such as audio and text, enabling multi-task learning approaches.
• Serves as a designated research baseline, facilitating reproducible comparisons in speech and audio ML.

Limitations

• Specific column structure, sample data, and exact row count are unknown, complicating direct usage.
• The dataset's origin, collection methodology, and speaker demographics are not detailed, limiting analysis of potential biases.
• Last updated in June 2022, which may not reflect the latest advancements or data in the field.

Provenance

Source

huggingface

Freshness

2022-06-21

Geography

us