This paper introduces UnSLU-BENCH, the first benchmark for machine unlearning in spoken language understanding (SLU), evaluating eight unlearning techniques across various datasets and models. It also proposes the Global Unlearning Metric (GUM) to comprehensively assess efficacy, utility, and efficiency in unlearning requests, particularly concerning speaker-specific data for privacy.