SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation
This addresses the problem of interpretability in speech-to-text generation for users needing transparent AI models, though it is incremental as it builds on existing feature attribution methods.
The paper tackles the lack of explainable AI methods for speech-to-text generation by introducing SPES, a feature attribution technique that provides fine-grained, phonetically meaningful explanations for each predicted token, and demonstrates its effectiveness in speech recognition and translation with faithful and plausible results.
Spurred by the demand for interpretable models, research on eXplainable AI for language technologies has experienced significant growth, with feature attribution methods emerging as a cornerstone of this progress. While prior work in NLP explored such methods for classification tasks and textual applications, explainability intersecting generation and speech is lagging, with existing techniques failing to account for the autoregressive nature of state-of-the-art models and to provide fine-grained, phonetically meaningful explanations. We address this gap by introducing Spectrogram Perturbation for Explainable Speech-to-text Generation (SPES), a feature attribution technique applicable to sequence generation tasks with autoregressive models. SPES provides explanations for each predicted token based on both the input spectrogram and the previously generated tokens. Extensive evaluation on speech recognition and translation demonstrates that SPES generates explanations that are faithful and plausible to humans.