Whisperization and Masked CycleGAN-Based Framework for Electrolaryngeal Speech Enhancement

Electrolarynx (EL) provides an effective approach to voice rehabilitation for patients with phonation disorder. However, due to its reliance on an external mechanical source, EL speech suffers from limited acoustic cues, leading to degraded quality and restricting the potential of subsequent modeling and enhancement. This paper proposes a novel EL speech enhancement framework that combines whisperization with Masked CycleGAN model. The whisperization step removes redundant constant excitation and mechanical noise, generating an intermediate speech form-whisper-like EL (W-EL) speech, whose acoustic and perceptual properties are closer to natural whisper. Subsequently, the Masked CycleGAN employs a frame-level masking strategy to guide the generator in reconstructing missing prosodic and linguistic features. Thus, we achieved a dual-stage enhancement of "redundancy removal" and "deficiency compensation." Acoustic feature analysis demonstrates that the converted W-EL speech is more similar to normal speech in terms of spectrogram, fundamental frequency (F0) values, and F0 contours, while also compensating for the missing low frequency energy below 500 Hz. Objective evaluations show significant improvements across multiple metrics. Subjective evaluations confirm that W-EL speech exhibits higher naturalness and intelligibility compared to original EL speech. Moreover, the combined "whisperization + voice conversion" framework further enhances perceptual quality. This study not only offer a novel pathway for EL speech enhancement, but also may provide valuable insights for improving other types of pathological speech.