Voiceprint Recognition based on Machine Learning Methods
Ajinkya Kunjir · Journal of Science and Engineering Research · 2022
Biometric identification technology has been widely used in today's society because of its convenience and security.As an important biometric feature, speech contains abundant information, and because of the popularity of smart devices, the collection cost of a speaker's speech is also very low.Therefore, it is of great practical value to analyze the speaker's voice.This paper mainly discusses the speaker's voiceprint recognition based on deep learning and expands the speech emotion recognition.Voiceprint recognition is divided into two tasks: speaker identification and speaker confirmation, and speech emotion recognition will be directly treated as a multi-classification problem.To take advantage of different attention mechanisms, this paper proposes a dual-path attention mechanism, which applies self-attention and convolution module attention at the same time and significantly improves the recognition effect without increasing the training time.Based on the ternary loss of predecessors, the cluster domain loss is proposed, and this paper further improves this loss for the speaker identification task and puts forward the weighted cluster domain loss, which pays more attention to the increase of the difference between classes, thus increasing the probability that critical samples are correctly classified.To solve the problem of low efficiency of cluster loss in the early stage of training, this paper also puts forward a novel loss function-critical enhancement loss, which pays extra attention to the sample pairs that are easiest and necessary to be optimized in every step of the training process.After combining cluster loss, the samples that are most difficult to optimize and easiest to optimize in every step are considered at the same time, which accelerates the training process and wins more training time for the difficult loss in cluster loss, thus further improving the final optimization effect.Aiming at the task of speaker emotion recognition, this paper proposes a lightweight neural network that combines Res Net and GRU.Compared with other methods in newer literature, this paper achieves comparable emotion classification results on the IEMOCAP data set with fewer parameters and features, in which UA reaches 67.9%, F1 score reaches 0.675, and the number of parameters is relatively reduced by 16.2%.