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。

MAD Speech：语音声音多样性的度量

MAD Speech: Measures of Acoustic Diversity of Speech

Sound event localization and detection (SELD) is an important task in machine
listening. Major advancements rely on simulated data with sound events in
specific rooms and strong spatio-temporal labels. SELD data is simulated by
convolving spatialy-localized room impulse responses (RIRs) with sound
waveforms to place sound events in a soundscape. However, RIRs require manual
collection in specific rooms. We present SpatialScaper, a library for SELD data
simulation and augmentation. Compared to existing tools, SpatialScaper emulates
virtual rooms via parameters such as size and wall absorption. This allows for
parameterized placement (including movement) of foreground and background sound
sources. SpatialScaper also includes data augmentation pipelines that can be
applied to existing SELD data. As a case study, we use SpatialScaper to add
rooms to the DCASE SELD data. Training a model with our data led to progressive
performance improves as a direct function of acoustic diversity. These results
show that SpatialScaper is valuable to train robust SELD models.

SpatialScaper 是一种用于模拟和增强 SELD 数据的库，可以通过模拟虚拟房间的参数化放置（包括移动）前景和背景声源，从而训练出具有更好性能的鲁棒 SELD 模型。