A Survey on Data Selection for Efficient Speech Processing

While recent advances in speech processing have led to substantial performance improvements across diverse tasks, they often demand significantly higher computational costs and resources. To address this efficiency challenge, data selection has emerged as a crucial strategy. This survey provides a comprehensive overview and introduces a unifying taxonomy for data selection methods in speech processing, structured along three key dimensions: selection granularity (sample-level vs. segment-level), selection process (static, dynamic, or active learning), and selection criteria (uncertainty, diversity, or hybrid approaches). Through systematic analysis across major speech tasks, including automatic speech recognition, text-to-speech synthesis, audio anti-spoofing, speaker recognition, and emotion recognition, we evaluate the effectiveness and applicability of diverse data selection strategies. Our analysis reveals that targeted data selection not only alleviates computational burdens but often enhances model robustness and performance by strategically filtering redundant, noisy, or detrimental training examples. By synthesizing insights scattered across disparate speech domains, we identify common principles, highlight task-specific challenges, and reveal emerging research trends. Finally, we outline promising future research directions in data selection for efficient speech processing.