Embeddings for DNN speaker adaptive training
Table 2: EERs (%) for i-vectors (per utt.), x-vectors, and deep embeddings evaluated in speaker recognition task with different backends. x-vector uses a model trained on AMI training data. x-vector (pretrained) uses Kaldi pretrained SRE16 x-vector model. x-vector (AMI backend) also uses the pretrained model but the LDA and PLDA transforms are trained on the AMI training data.
['[EMPTY]', 'cosine', 'PLDA', 'LDA', 'LDA/']
We use the Equal Error Rate (EER) metric. Deep CNN embeddings extracted with a 800D PCA transformation are the most accurate speaker representations. Comparing i-vectors and x-vectors, we find that i-vectors are superior with all backends. For very short utterances (< 1 sec), deep CNN embeddings are able to capture speaker identity better than for longer utterances.