  In this paper, we propose a Convolutional Neural Network (CNN) based speaker
recognition model for extracting robust speaker embeddings. The embedding can
be extracted efficiently with linear activation in the embedding layer. To
understand how the speaker recognition model operates with text-independent
input, we modify the structure to extract frame-level speaker embeddings from
each hidden layer. We feed utterances from the TIMIT dataset to the trained
network and use several proxy tasks to study the networks ability to represent
speech input and differentiate voice identity. We found that the networks are
better at discriminating broad phonetic classes than individual phonemes. In
particular, frame-level embeddings that belong to the same phonetic classes are
similar (based on cosine distance) for the same speaker. The frame level
representation also allows us to analyze the networks at the frame level, and
has the potential for other analyses to improve speaker recognition.
