Improving Noise Robustness In Speaker Recognition Using A Two-Stage Attention Model
Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB.
['[ITALIC] γ', '[BOLD] TDNN+Para Top1 (%)', '[BOLD] TDNN+Para EER (%)', '[BOLD] CNN+Para Top1 (%)', '[BOLD] CNN+Para EER (%)']
To further test and compare the performance of parallel scenario (Para), the weight value γ in para scenario is tuned from 0 to 1. The different γ value is tested on Babble noise type and SNR value is equals to Results show that with the increase of γ, the accuracies become higher and the equal error rate lower down. For TDNN model, it reaches a peak when γ is equals to 0.6, and when γ =0.8, CNN model reaches its peak. This results shows that the frequency attention in both TDNN and CNN architectures contributes more to the recognition results, it also shows a possible reason why the results of time attention first (T-F) is worse than that of frequency attention first (F-T).