Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition
This work addresses the challenge of designing efficient neural architectures for industrial-scale speech recognition systems, showing that learned architectures can outperform hand-designed ones.
The authors tackled the problem of neural architecture search for automatic speech recognition by proposing a revised search space that facilitates finding architectures with low complexity, achieving more than 20% and 15% relative improvements on test sets compared to hand-designed architectures.
In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. With reference to the previous works in the computer vision field, the transferability of the searched architecture is the main focus of our work. The architecture search is conducted on the small proxy dataset, and then the evaluation network, constructed with the searched architecture, is evaluated on the large dataset. Especially, we propose a revised search space for speech recognition tasks which theoretically facilitates the search algorithm to explore the architectures with low complexity. Extensive experiments show that: (i) the architecture searched on the small proxy dataset can be transferred to the large dataset for the speech recognition tasks. (ii) the architecture learned in the revised search space can greatly reduce the computational overhead and GPU memory usage with mild performance degradation. (iii) the searched architecture can achieve more than 20% and 15% (average on the four test sets) relative improvements respectively on the AISHELL-2 dataset and the large (10k hours) dataset, compared with our best hand-designed DFSMN-SAN architecture. To the best of our knowledge, this is the first report of NAS results with large scale dataset (up to 10K hours), indicating the promising application of NAS to industrial ASR systems.