Which Evaluation for Which Model? A Taxonomy for Speech Model Assessment
Abstract
Speech foundation models have recently achieved remarkable capabilities across a wide range of tasks. However, their evaluation remains disjointed across tasks and model types. Different models excel at distinct aspects of speech processing and thus require different evaluation protocols. This paper proposes a unified taxonomy that addresses the question: Which evaluation is appropriate for which model? The taxonomy defines three orthogonal axes: the \textbf{evaluation aspect} being measured, the model capabilities required to attempt the task, and the task or protocol requirements needed to perform it. We classify a broad set of existing evaluations and benchmarks along these axes, spanning areas such as representation learning, speech generation, and interactive dialogue. By mapping each evaluation to the capabilities a model exposes (e.g., speech generation, real-time processing) and to its methodological demands (e.g., fine-tuning data, human judgment), the taxonomy provides a principled framework for aligning models with suitable evaluation methods. It also reveals systematic gaps, such as limited coverage of prosody, interaction, or reasoning, that highlight priorities for future benchmark design. Overall, this work offers a conceptual foundation and practical guide for selecting, interpreting, and extending evaluations of speech models.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a capability-aware taxonomy for evaluating speech models, which systematically organizes evaluation tasks and provides a framework for selecting appropriate assessments based on model characteristics. This work is significant as it addresses the fragmented landscape of speech model evaluations and offers a structured approach to improve the alignment between models and their evaluation methods.
Methodology
The paper introduces a structured taxonomy for evaluating speech foundation models, defining three orthogonal axes: evaluation aspects, model capabilities, and task requirements. This approach is methodologically sound as it systematically categorizes existing evaluation benchmarks and provides a framework for aligning models with appropriate evaluation methods. The authors effectively illustrate the taxonomy with examples, making it a practical tool for researchers and practitioners in the field.
Results & evaluation
While the paper does not present original experimental results, it reviews a broad set of existing evaluations and benchmarks, categorizing them according to the proposed taxonomy. This comprehensive mapping highlights the diversity of evaluation practices and identifies gaps in current methodologies, particularly in areas like prosody and interaction.
Reproducibility
The taxonomy framework is well-defined, allowing for reproducibility in terms of how evaluations can be selected based on model capabilities. However, the paper lacks specific implementation details or datasets that would enable direct replication of experiments or evaluations.
Limitations
The paper does not conduct original experiments, which limits its ability to validate the proposed taxonomy through empirical data. Additionally, while it identifies gaps in existing evaluations, it does not propose concrete solutions or new benchmarks to address these gaps.
Broader impact
The proposed taxonomy has significant implications for the field of speech processing, as it provides a comprehensive framework for evaluating diverse speech models. By clarifying which evaluations are suitable for different model types, it can guide future research and development in speech technology, ultimately enhancing the effectiveness of speech applications in real-world scenarios. The main contribution of this paper is the introduction of a capability-aware taxonomy for evaluating speech models, which systematically organizes evaluation tasks and provides a framework for selecting appropriate assessments based on model characteristics. This work is significant as it addresses the fragmented landscape of speech model evaluations and offers a structured approach to improve the alignment between models and their evaluation methods.
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