H-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336
['Utterance Length', 'Model', 'Accuracy %']
Two different utterance lengths, 1 second and 3 seconds, are used in the experiments, respectively. The use of the H-vectors shows higher accuracy when using either 1-second or 3-second input length than the two baselines. When the length of input utterances is one second, the accuracy obtained using the H-vectors can reach 94.5%, with 4.4% improvement over X-vectors and 2.4% improvement over X-vectors+Attention, respectively. When the length of input utterances is three seconds, the accuracy obtained using the H-vectors can reach 98.5%, with about 3% improvement over X-vectors and about 2% improvement over X-vectors+Attention. The proposed approach is more robust than the two baselines when processed utterances are short. In addition, the accuracies obtained using 3-second utterances are better than those using 1-second utterances. As longer utterance contains more information relevant to a target speaker than those in short ones.