AM-MobileNet1D: A Portable Modelfor Speaker Recognition
TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size.
['[EMPTY]', 'TIMIT FER (%)[↓]', 'TIMIT CER (%)[↓]', 'MIT FER (%)[↓]', 'MIT CER (%)[↓]', 'Inference Time (ms)[↓]', 'Parameters (total)[↓]', 'Model Size (mb)[↓]']
The best result in each dataset is highlighted in bold. Based on these results, it is possible to see that in every scenario, the AM-Softmax loss function improved the performance of the models. Indeed, the benefit of using the AM-Softmax loss function on the Speaker Recognition models is quite clear, mainly on the SincNet model for the TIMIT dataset. The FER and CER calculated on the TIMIT dataset for the proposed method MobileNet1D are about 40% and 50% smaller (better) than the ones measured from SincNet, respectively. Also, when comparing the MobileNet1D with the AM-SincNet in the same dataset, they are practically equivalent, the MobileNet1D got a slightly better FER, while the AM-SincNet got a slightly better CER. For this dataset, the model with the best results was the proposed AM-MobileNet1D, it got the lowest (best) FER and CER over the models. In the same way, in terms of CER it was 24% better than the MobileNet1D, 14% better than the AM-SincNet, and 62% better than the SincNet. On the other hand, the SincNet and the AM-SincNet overcome the proposed models in terms of FER and CER for the MIT dataset. Still, the proposed models are quite good on this dataset showing an accuracy of about 98%. The inference time results are in the format time±standard deviation. The results show that the proposed models are about 7 times faster, have 8 times fewer parameters, and take up to 8 times less space on disk than the SincNet and the AM-SincNet. The proposed MobileNet1D and AM-MobileNet1D take only 11.6 megabytes on disk, against 91.2mb from the SincNet and AM-SincNet. In fact, these results show that the proposed models are suitable for being embedded on Mobile devices, showing high performance and low inference latency for the speaker recognition problem.