End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning
Table 1: Results (WER, %) with different speaker embedding inclusion strategies on the two-speaker overlapped speech dataset.
['Strategy', 'dev', 'eval']
The first set of experiments aims to determine the best strategy for inclusion of speaker embeddings in the model’s input. While vertical stacking does not enforce same size of the speaker embedding and acoustic features, we perform two experiments with vertical stacking: the first one with unchanged sizes of the input vectors and the second one with the downscaled speaker embedding. The second experiment isolates the effect of different stacking types from the effect of the speaker embedding downscaling. 50 utterances of reference speech are used to produce each speaker embedding in this experiment.