Embeddings for DNN speaker adaptive training
Table 1: Comparison of the approaches to generate the parameters acting on the embeddings for input features and hidden representations transformation. All models (except for the SI baseline) use CMN features.
['[ITALIC] Embed. mapping', 'shift/scale', 'WER']
The first block gives baseline results, the second one is for input features transformation, and the third is for hidden representation transformations. Different patterns can be observed depending on where in the network the transformation is applied. For input feature transformation, a multi-layer control network did not outperform the CMN baseline. All of the approaches with fewer parameters were superior to the control network. Since the control network is the most flexible, this suggests that when the control network is applied as low in the network as to the input layer the model may be more vulnerable to overfitting.