Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models

Summary:This research introduces a novel diagnostic framework for analyzing performance bottlenecks in speech language models during the audio-to-answer transformation process. By comparing the emitted answer, option logits, affine readout, and linear readout of the hidden state, the study reveals that state decoding outperforms generation by 27.8 accuracy points on average. The framework identifies actionable gaps in decision rules and readout coverage and demonstrates that label-free logit correction

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Background and Motivation

In recent years, speech language models have seen widespread application in areas such as speech recognition and sentiment analysis. However, existing evaluation methods primarily focus on the accuracy of the emitted answers, overlooking potential performance bottlenecks at different processing stages. This study aims to address this gap by introducing a new diagnostic framework that provides a detailed analysis of the model's behavior during the audio-to-answer transformation process.

Key Contributions

1. Diagnostic Framework: A generation-aligned diagnostic ladder is proposed, comparing the emitted answer, option logits, affine readout, and linear readout of the hidden state.
2. Bottleneck Identification: The study finds that state decoding outperforms generation by 27.8 accuracy points on average, and both decision-rule and readout-coverage gaps are positive across all conditions.
3. Improvement Validation: A label-free logit correction method is demonstrated to improve generated accuracy, showcasing the actionable nature of the decision-rule gap.

Technical Highlights

• Separation Analysis: For the first time, the model performance bottlenecks are separated into endpoint, decision-rule, and readout-coverage dimensions, providing a new perspective for understanding model behavior.
• Generalization Verification: The framework is tested on five systems and two emotion corpora, confirming its general applicability and effectiveness.
• Label-Free Correction: A novel method for logit correction without additional label information is proposed, demonstrating its potential for enhancing model performance.

Industry Impact and Developer Recommendations

• Impact on AI Research: The study provides new tools and methods for evaluating and improving the performance of speech language models, contributing to advancements in the field.
• Recommendations for Developers: Developers are advised to focus on improving decision rules and readout coverage and to consider applying label-free correction methods to enhance model performance.
• Future Research Directions: Future research could explore the application of the diagnostic framework to other types of language models and develop more efficient correction methods.

Conclusion

The proposed diagnostic framework offers a new tool for evaluating and improving the performance of speech language models, highlighting the importance of decision rules and readout coverage in model performance and providing valuable insights for future research.

Source: arXiv:2608.06409

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Tags: #Speech Language Models #Diagnostic Framework #Performance Evaluation #Decision Rules #State Decoding