Advances in Speech Separation: Techniques, Challenges, and Future Trends

TL;DR

This survey comprehensively reviews DNN-based speech separation techniques, analyzing learning paradigms, architectures, and emerging trends, providing current benchmarks and insights for future research directions.

Contribution

It offers a systematic, comprehensive examination of speech separation methods, including recent innovations, benchmarks, and future promising directions, filling a gap in the literature.

Findings

Evaluation of various architectures and learning paradigms.

Identification of emerging trends like domain-robust and multimodal methods.

Benchmarking of methods on standard datasets.

Abstract

The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation techniques. Our work differentiates itself through: (I) Comprehensive perspective: We systematically investigate learning paradigms, separation scenarios with known/unknown speakers, comparative analysis of supervised/self-supervised/unsupervised frameworks, and architectural components from encoders to estimation strategies. (II) Timeliness: Coverage of cutting-edge developments ensures access to current innovations and benchmarks. (III) Unique insights: Beyond summarization, we evaluate technological trajectories, identify emerging patterns, and highlight promising directions including domain-robust frameworks, efficient architectures, multimodal integration, and novel self-supervised paradigms. (IV) Fair evaluation: We provide quantitative evaluations on standard datasets, revealing true capabilities and limitations of different methods. This comprehensive survey serves as an accessible reference for experienced researchers and newcomers navigating speech separation's complex landscape.