Multi-Scale Channel Adaptive Time-Delay Neural Network and Balanced Fine-Tuning for Arabic Dialect Identification

Abstract

The time-delay neural network (TDNN) can consider multiple frames of information simultaneously, making it particularly suitable for dialect identification. However, previous TDNN architectures have focused on only one aspect of either the temporal or channel information, lacking a unified optimization for both domains. We believe that extracting appropriate contextual information and enhancing channels are critical for dialect identification. Therefore, in this paper, we propose a novel approach that uses the ECAPA-TDNN from the speaker recognition domain as the backbone network and introduce a new multi-scale channel adaptive module (MSCA-Res2Block) to construct a multi-scale channel adaptive time-delay neural network (MSCA-TDNN). The MSCA-Res2Block is capable of extracting multi-scale features, thus further enlarging the receptive field of convolutional operations. We evaluated our proposed method on the ADI17 Arabic dialect dataset and employed a balanced fine-tuning strategy to address the issue of imbalanced dialect datasets, as well as Z-Score normalization to eliminate score distribution differences among different dialects. After experimental validation, our system achieved an average cost performance (Cavg) of 4.19% and a 94.28% accuracy rate. Compared to ECAPA-TDNN, our model showed a 22% relative improvement in Cavg. Furthermore, our model outperformed the state-of-the-art single-network model reported in the ADI17 competition. In comparison to the best-performing multi-network model hybrid system in the competition, our Cavg also exhibited an advantage.