Multi-language Speaker Recognition using Multi-feature Fusion and Deep Learning Technique: A Survey
Authors: Sunil D. Kale  
Published: January 2025

Automatic speaker recognition has come a long way, thanks to deep learning technologies, but identifying speakers across multiple languages still presents some difficulties. This survey looks at the latest developments in speaker identification, focusing on how combining different features with deep learning techniques has improved performance. It covers a range of methods for extracting important speech characteristics, from traditional features like MFCCs to those that take language-specific sounds into account, while also tackling issues like background noise and variations in speech. The paper also dives into different deep learning models, including DNNs, CNNs, and RNNs, to show how they help with speaker identification. Additionally, it highlights the need for big and varied datasets to help create models that work well for different languages, addressing challenges like a lack of data and differences in speaking styles. Overall, this review gives a clear picture of where multi-language speaker identification stands today and points out areas where more research is needed.
Introduction: Automatic speaker recognition (ASR) identifies individuals using unique vocal features influenced by vocal tract and speech production differences. It performs two tasks: speaker identification, determining a speaker’s identity, and speaker verification, confirming a claimed identity.
Advancements in deep learning, including models like DNNs, CNNs, and RNNs, have significantly improved ASR’s accuracy and reliability. The process involves two phases: enrolment, where the system learns voice features, and recognition, where it classifies voices based on these features.
Objectives: This survey aims to evaluate advancements in multi-language speaker recognition, focusing on the integration of traditional feature extraction methods and deep learning models to improve recognition accuracy. The objectives include exploring noise-resilient techniques, dataset diversity, and challenges in multilingual and low-resource settings.
Methods: This study investigates speaker recognition across multiple languages by leveraging traditional and deep learning-based approaches. Key techniques include feature extraction using MFCCs, i-vectors, and x-vectors, combined with advanced neural networks like CNNs and RNNs. Comparative analyses of text-dependent and text-independent methods, along with noise-handling strategies, are also explored to enhance accuracy and robustness.
Results: The findings highlight significant advancements in multi-language speaker recognition, showcasing the effectiveness of deep learning models like CNNs, DNNs, and x-vectors in improving accuracy under challenging conditions. Comparative analyses reveal improved performance in multilingual, noisy, and low-resource environments.
Conclusions: The survey highlights advancements in multi-language speaker recognition, with deep learning models like CNNs, DNNs, and RNNs significantly improving accuracy in noisy and multilingual scenarios. Techniques such as x-vectors, ResNet architectures, and hybrid models show promise, but challenges remain in reducing data requirements and enhancing robustness to speaker variability. Future work should focus on low-resource environments, integrating with other biometrics, and addressing ethical concerns like privacy and bias for responsible deployment.