Abstract
Spoken language understanding (SLU) models are commonly evaluated based on overall performance or predefined subgroups, often overlooking the potential insights gained from more comprehensive subgroup analyses. Conducting a more thorough analysis at the subgroup level can reveal valuable insights into the variations in speech system performance across different subgroups. Yet, identifying interpretable subgroups in raw speech data poses inherent challenges. To overcome these issues, we enrich speech data with metadata from various domains. We consider, when available, speaker demographics like gender, age, and origin country. We also incorporate taskrelated features, such as a specific intent or emotion associated with an utterance. Finally, we extract signal-related metadata, including speaking rate, signal-to-noise ratio, number of words, and number of pauses. Including these features, extracted directly from the raw signal, is crucial in capturing fine-grained nuances that may impact model performance. By combining these metadata, we identify human-understandable subgroups in which speech models exhibit performance significantly better or worse than the average. Our approach is task-, model-, and dataset-agnostic. It enables the identification of intra- and crossmodel performance gaps, highlighting disparities among different models. We validate our methodology across three tasks (intent classification, automatic speech recognition, and emotion recognition), three datasets, and one speech model with different sizes, providing nuanced insights into model assessments. We further propose leveraging this approach to guide a data acquisition strategy for improved and fairer models. The experimental results demonstrate that our approach leads to substantial performance improvements and significant reductions in performance disparities, all achieved with reduced data and costs compared to random and clustering-based acquisition techniques