CACRN-Net: A 3D log Mel spectrogram based channel attention convolutional recurrent neural network for few-shot speaker identification

Advancements in deep learning for speaker identification are constrained by the limited availability of data, especially in law enforcement applications. This has led to the emergence of few-shot speaker identification, a technique that classifies unseen test samples with the help of a few support samples. Despite several attempts to advance few-shot speaker identification, significant challenges persist, including the extraction of robust speaker embeddings, the problem of overfitting, and the issue of prototype shift error. This paper proposes a few-shot speaker identification system employing a novel architecture called the Channel Attention-based Convolutional Recurrent Neural Network (CACRN-Net) with three-dimensional (3D) log Mel spectrogram inputs to mitigate overfitting and enhance the accuracy of speaker embeddings. Furthermore, a self-attention mechanism alleviates prototype shift errors caused by noisy data. The proposed framework is compared to existing methods using VCTK and Voxceleb1 speech corpora through 5-way, 5-shot learning experiments. To assess the performance of the framework in speech variability conditions, we utilized the IIT Guwahati (IITG) multi-variability (MV) speech database. The proposed approach outperforms state-of-the-art techniques, achieving a substantial enhancement in speaker identification with a 2.73 % accuracy improvement on the VCTK database and a 2.3 % improvement on Voxceleb1.