Modern speech applications require compact embeddings that generalize across both linguistic and paralinguistic tasks. However, most existing embeddings are task-specific and fail to transfer effectively across domains. We propose wavCSE, a feature-based multi-task learning model that produces a fixed-size unified speech embedding suitable for both linguistic and paralinguistic tasks. wavCSE is jointly trained on keyword spotting, speaker identification, and emotion recognition, achieving state-of-the-art performance on all three tasks. The resulting unified embedding is then evaluated on twelve downstream tasks spanning both linguistic and paralinguistic domains. Experimental results show that it outperforms strong baselines on nine of the twelve tasks, indicating effective generalization across domains. To streamline embedding generation, we introduce a recursive layer selection strategy that identifies the most informative hidden layer outputs from the upstream model and refine how these selected outputs are aggregated in the downstream model. These enhancements reduce memory usage and computational cost while improving task performance, making them broadly applicable to self-supervised learning-based speech processing models.

Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems.