EfficientTDNN: Efficient Architecture Search for Speaker Recognition

TL;DR

EfficientTDNN introduces an automated neural architecture search framework for speaker recognition, significantly reducing computational costs while discovering highly effective TDNN architectures tailored to various resource constraints.

Contribution

The paper presents a novel EfficientTDNN framework combining a supernet with NAS to automate and optimize TDNN architecture design for speaker recognition tasks.

Findings

Achieves state-of-the-art EER with fewer MACs on VoxCeleb

Enables exploration of over 10^13 architectures efficiently

Supernet generalizes well to unseen subnets, balancing accuracy and efficiency

Abstract

Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have shown their remarkable capability in learning speaker embedding. However, they meanwhile bring a huge computational cost in storage size, processing, and memory. Discovering the specialized CNN that meets a specific constraint requires a substantial effort of human experts. Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken language processing tasks such as speaker recognition. In this paper, we propose EfficientTDNN, an efficient architecture search framework consisting of a TDNN-based supernet and a TDNN-NAS algorithm. The proposed supernet introduces temporal convolution of different ranges of the receptive field and feature aggregation of various resolutions from different layers to TDNN. On top of it, the TDNN-NAS algorithm quickly searches for the desired TDNN architecture via weight-sharing subnets, which surprisingly reduces computation while handling the vast number of devices with various resources requirements. Experimental results on the VoxCeleb dataset show the proposed EfficientTDNN enables approximate $10^{13}$ architectures concerning depth, kernel, and width. Considering different computation constraints, it achieves a 2.20% equal error rate (EER) with 204M multiply-accumulate operations (MACs), 1.41% EER with 571M MACs as well as 0.94% EER with 1.45G MACs. Comprehensive investigations suggest that the trained supernet generalizes subnets not sampled during training and obtains a favorable trade-off between accuracy and efficiency.