Embeddings for DNN speaker adaptive training
Table 4: WERs for DNN-SAT with different embeddings. Models are trained with a control layer used to shift the input features.
['[ITALIC] Embedding type', 'WER']
We evaluate the influence of the type of embedding and its characteristics on the DNN-SAT task, using a control layer to shift the input features. By replacing online i-vectors with offline utterance i-vectors, we gained 0.1% in WER, thus the frame level variation of the embeddings did not contribute to better WER in our experiments. On the other hand, online decoding can only be performed with online representations, and as the loss of performance is not substantial, one may prefer to use online i-vectors. It is important to bear in mind however, that genuine speaker labels were used for online i-vector extraction in our experiments. For the deployment of DNN-SAT with online representations, an additional diarization or speaker clustering step would be needed.