  Speaker embedding is an important front-end module to explore discriminative
speaker features for many speech applications where speaker information is
needed. Current SOTA backbone networks for speaker embedding are designed to
aggregate multi-scale features from an utterance with multi-branch network
architectures for speaker representation. However, naively adding many branches
of multi-scale features with the simple fully convolutional operation could not
efficiently improve the performance due to the rapid increase of model
parameters and computational complexity. Therefore, in the most current
state-of-the-art network architectures, only a few branches corresponding to a
limited number of temporal scales could be designed for speaker embeddings. To
address this problem, in this paper, we propose an effective temporal
multi-scale (TMS) model where multi-scale branches could be efficiently
designed in a speaker embedding network almost without increasing computational
costs. The new model is based on the conventional TDNN, where the network
architecture is smartly separated into two modeling operators: a
channel-modeling operator and a temporal multi-branch modeling operator. Adding
temporal multi-scale in the temporal multi-branch operator needs only a little
bit increase of the number of parameters, and thus save more computational
budget for adding more branches with large temporal scales. Moreover, in the
inference stage, we further developed a systemic re-parameterization method to
convert the TMS-based model into a single-path-based topology in order to
increase inference speed. We investigated the performance of the new TMS method
for automatic speaker verification (ASV) on in-domain and out-of-domain
conditions. Results show that the TMS-based model obtained a significant
increase in the performance over the SOTA ASV models, meanwhile, had a faster
inference speed.
