Improved RawNet with Feature Map Scaling forText-independent Speaker Verification using Raw Waveforms
Table 3: Various applications of the proposed FMS. Baseline refers to the modified RawNet (Table 2). Mechanism addresses variations of applying the proposed method. c is the feature map, and s is the scale vector used to conduct FMS derived from c. “sep” indicates using separate scale vectors for additive and multiplicative scaling. Performance is reported in terms of EER and RER.
['System', 'Mechanism', 'EER', 'RER']
Systems #4 and #5 show the results obtained using the attention and multi-head attention mechanisms using the softmax-based exclusive attention map on the filter domain. In addition, the experimental results obtained by replacing the GRU layer with self-attentive pooling or self-multi-head-attentive pooling reduced performance. These results demonstrate that, in the case of RawNet, the GRU better aggregates frame-level representations into an utterance-level representation. Among application of various related methods, System #6 demonstrated the best result. Here, the “Mechanism” column shows how we performed the proposed FMS method. System #8 and System #9 applied the two proposed methods, and #10 and #11 applied both methods at the same time in different order. The results show that the proposed methods yielded improvements with RERs of 6.00 % and 11.33 %. Applying both methods simultaneously further improved the performance, and System #11 demonstrated an EER of 2.56 %. System #12 shows the result obtained using separate scale vectors for additive and multiplicative FMS. As shown, additional improvements were not observed.