Speech Emotion Recognition Data Using MFCC Features and Deep Learning

Audio data for speech emotion recognition, likely containing speech recordings and extracted features. The dataset is sourced from Kaggle and utilizes Mel-frequency cepstral coefficients (MFCC) for feature extraction. The specific collection methodology, size, and authorship are not detailed in the available metadata.

Use Cases

• Train deep learning models for emotion classification based on MFCC features.
• Benchmark speech emotion recognition algorithms using a common feature extraction method.
• Study the relationship between acoustic features and emotional states in speech.

Strengths

• Focuses on a specific and well-defined task of speech emotion recognition.
• Employs a standard audio feature extraction technique (MFCC) commonly used in the field.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle