 In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two  hyper-parameters that heavily affect the performance and model complexity of state-of-the-art factored time delay neural network (TDNN-F)acoustic models : i) the left and right splicing context offsets;
<clarity>  In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two  hyper-parameters that heavily affect the performance and model complexity of state-of-the-art factored time delay neural network (TDNN-F)acoustic models : i) the left and right splicing context offsets;
 In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two hyper-parameters that heavily affect the performance and model complexity of state-of-the-art factored time delay neural networks (TDNNs) : i) the left and right splicing context offsets;