Learning Speech Representations from Raw Audioby Joint Audiovisual Self-Supervision
Table 2: Results for spoken word classification (Accuracy in %) under various levels of introduced noise (SNR in dB). Babble noise from the NOISEX database is used to perturb the audio samples in the LRW and SPC datasets.
['Dataset', 'Model', 'Noise level (SNR) -5 dB', 'Noise level (SNR) 0 dB', 'Noise level (SNR) 5dB', 'Noise level (SNR) 10 dB', 'Noise level (SNR) 15 dB', 'Noise level (SNR) 20 dB', 'Noise level (SNR) Clean']
We also compare the performance of the variations of our method under various levels of artificially induced noise. All our methods outperform MFCCs at all noise levels on both datasets. The joint audiovisual method is the best.