5.8.2 Datasets
Voice activity detection models can be trained and evaluated using various datasets, each with
unique features. The TIMIT dataset is popular, providing, 6300 phonetically transcribed utterances
from 630 speakers. On the other hand, CHiME-5 is designed for speech separation and recognition
in real-world environments and includes multichannel recordings of 20 speakers in locations such
as cafés, buses, and pedestrian areas. Despite its primary purpose, CHiME-5 is widely used for
voice activity detection. AURORA-4 is specifically designed to evaluate the robustness of ASR
systems and contains over 10, 000 in noisy speech utterances recorded in environments like car
noise, babble noise, and street noise. It is also extended to VAD for evaluating challenging scenarios.
DEMAND is a suitable dataset for evaluating VAD algorithms as it includes over 1200 artificially
created noise signals with various noise types like white noise, pink noise, and café noise. Finally,
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