SPEAKER-AWARE SPEECH-TRANSFORMER
Table 4: CER[%] for SAST with access to different number of i-vectors in AISHELL-2. Gains are relative to our ST baselilne on test set.
['#I-vectors', 'Dev', 'Test', 'Gain']
We can see that the proposed SAST still works quite well even if the i-vectors in SKB all comes from a different data source other than the acoustic training set. We think that this can be attributed to the power of soft speaker embedding. The i-vectors in SKB form a speaker space, and the SAST model is trained to represent each speaker as the combination of data points in this space, even for speakers not belonging to SKB.