Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition
Table 2: WERs (%) for two-speaker-mixed test-clean. Main output branch was used for the target speaker’s ASR and auxiliary output branch was used for the interference speaker’s ASR.
['SIR of target spk.', 'SIR of interference spk.', 'WER (%) target spk.', 'WER (%) interference spk.']
Therefore, we evaluated the ability of an auxiliary network for the secondary ASR. In this evaluation, we provided the target speaker’s embeddings for the network, and evaluated the WERs of the ASR results using the output of the auxiliary output branch. From this table, we found that the auxiliary output branch worked very well for the secondary ASR. This result clearly indicated that the shared layers of the network were learned to realize speaker separation as we expected. In addition, we believe this secondary ASR itself is attractive as exemplified above. Different from PIT-ASR models, our model can explicitly output not only transcriptions of the target speaker but also those of other speakers in a consistent order across utterances.