Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech

Abstract

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection. The aim of this paper is to propose a simple and constrained convolutional neural network for speaker recognition tasks and to examine its robustness for recognition in emotional speech conditions. We examine three quantization methods for developing a constrained network: floating-point eight format, ternary scalar quantization, and binary scalar quantization. The results are demonstrated on the recently recorded SEAC dataset.

Enhancing Emotion Recognition through Federated Learning: A Multimodal Approach with Convolutional Neural Networks

“…Unlike the method proposed in [29], we do not utilize MFCC (Mel frequency cepstral coefficients) as inputs. Instead, we adopt the approach of generating spectrograms using short-time Fourier transform (STFT), following the methodology described in [30].…”