Speaker recognition refers to audio biometrics that utilizes acoustic characteristics for automatic speaker recognition . These systems have emerged as an essential means of verifying identity in various scenarios, such as smart homes, general business interactions, e-commerce applications, and forensics. However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild but neglect storage and computation requirements. To address this issue , we propose a neural architecture search-based efficient time-delay neural network (EfficientTDNN)  to improve inference efficiency while maintaining recognition accuracy. The proposed EfficientTDNN contains three phases . First, supernet designconstructs a dynamic neural architecture that consists of sequential cells and enables network pruning. Second , progressive training optimizes randomly sampled subnets that inherit the weights of the supernet . Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency. Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs. Comprehensive investigation suggests that the trained supernet generalizes cells unseen during training and obtains an acceptable balance between accuracy and efficiency.
Speaker recognition refers to audio biometrics that utilizes acoustic characteristics . These systems have emerged as an essential means of authenticating identity in various areas such as smart homes, general business interactions, e-commerce applications, and forensics. The mismatch between development and real-world data causes a shift of speaker embedding space and severely degrades the performance of speaker recognition. Extensive efforts have been devoted to address speaker recognition in the wild , but these often neglect computation and storage requirements. In this work , we propose an efficient time-delay neural network (EfficientTDNN) based on neural architecture search to improve inference efficiency while maintaining recognition accuracy. The proposed EfficientTDNN contains three phases : supernet design , progressive training , and architecture search. Firstly, we borrow the design of TDNN to construct a supernet that enables sampling subnets with different depth, kernel, and width. Secondly, the supernet is progressively trained with multi-condition data augmentation to mitigate interference between subnets and overcome the challenge of optimizing a huge search space. Thirdly, an accuracy predictor and efficiency estimator are proposed to use in the architecture search to derive the specialized subnet under the given efficiency constraints. Experimental results on the VoxCeleb dataset show EfficientTDNN achieves 1.55\% equal error rate (EER) and 0.138 detection cost function (DCF_{0.01 with 565M multiply-accumulate operations (MACs) as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs. Comprehensive investigations suggest that the trained supernet generalizes subnets not sampled during training and obtains a favorable trade-off between accuracy and efficiency.
D
for automatic speaker recognition
R
verifying
authenticating
R
scenarios,
areas
R
However, the mismatch between training
The mismatch between development
R
recognition performance . Various complicated neural architectures are presented
performance of speaker recognition. Extensive efforts have been devoted
R
but neglect storage and computation requirements. To address this issue
, but these often neglect computation and storage requirements. In this work
R
a neural architecture search-based
an
A
based on neural architecture search
R
. First, supernet designconstructs a dynamic neural architecture that consists of sequential cells and enables network pruning. Second
: supernet design
R
optimizes randomly sampled subnets that inherit the weights of the supernet . Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy
, and architecture search. Firstly, we borrow the design of TDNN to construct a supernet that enables sampling subnets with different depth, kernel,
R
efficiency. Results of experiments
width. Secondly, the supernet is progressively trained with multi-condition data augmentation to mitigate interference between subnets and overcome the challenge of optimizing a huge search space. Thirdly, an accuracy predictor and efficiency estimator are proposed to use in the architecture search to derive the specialized subnet under the given efficiency constraints. Experimental results
D
provides a vast search space including approximately 10^{13
R
EER
equal error rate (EER)
R
DCF_{0.01
detection cost function (DCF_{0.01
R
MACs
multiply-accumulate operations (MACs)
R
investigation suggests
investigations suggest
R
cells unseen
subnets not sampled
R
an acceptable balance
a favorable trade-off