Under the hood: Phonemic Restoration in transformer-based automatic speech recognition

This study investigates how the automatic speech recognition (ASR) models wav2vec 2.0 large-960h-lv60-self and Whisper large-v3 perform when segment-level signal perturbations (added noise, noisy gaps, and two types of silent gaps) are introduced in English words and pseudowords. We probed the speech embeddings throughout their encoder transformer layers to examine how they encode articulatory features (place and manner of articulation, and voicing). We found that wav2vec 2.0 was more successful than Whisper at restoring perturbed segments across conditions. For wav2vec 2.0 embeddings, classification accuracy was higher in words than in pseudowords. The articulatory features encoding of both ASR models was least disturbed by added noise, and most disturbed by noisy gaps, with silent gaps falling in between. Coarticulatory cues improved classification of articulatory features and classification accuracy increased from early to late layers for both models. Among the examined target sounds, [n] stood out from [m], , and [l], as it was classified particularly well under all conditions. We compare ASR performance to the Phonemic Restoration Effect in human speech perception and discuss potential reasons for the performance differences between the two ASR models. This approach aims to foster a better understanding of otherwise opaque systems. • Tracing phonetic cues in transformer-based ASR models with domain-informed probing. • Wav2vec 2.0 was more successful than Whisper at restoring perturbed speech segments. • Unlike Whisper, wav2vec 2.0 was less effective with pseudowords than with words. • In contrast to humans, ASR models were more affected by noisy gaps than silent gaps.