Generative spoken language models produce speech in a wide range of voices,
prosody, and recording conditions, seemingly approaching the diversity of
natural speech. However, the extent to which generated speech is acoustically
diverse remains unclear due to a lack of appropriate metrics. We address this
gap by developing lightweight metrics of acoustic diversity, which we
collectively refer to as MAD Speech. We focus on measuring five facets of
acoustic diversity: voice, gender, emotion, accent, and background noise. We
construct the metrics as a composition of specialized, per-facet embedding
models and an aggregation function that measures diversity within the embedding
space. Next, we build a series of datasets with a priori known diversity
preferences for each facet. Using these datasets, we demonstrate that our
proposed metrics achieve a stronger agreement with the ground-truth diversity
than baselines. Finally, we showcase the applicability of our proposed metrics
across several real-life evaluation scenarios. MAD Speech will be made publicly
accessible.

发展了一种声学多样性的轻量级度量标准集，通过专门的嵌入模型和一个聚合函数来衡量声学多样性，在多个真实评估场景中展示了其适用性。