EPG2S: Speech Generation and Speech Enhancement Based on Electropalatography and Audio Signals Using Multimodal Learning

• Published in: IEEE signal processing letters 2022, Vol.29, p.2582-2586
• Main Authors: Chen, Li-Chin, Chen, Po-Hsun, Tsai, Richard Tzong-Han, Tsao, Yu
• Format: Article
• Language: English
• Subjects: Audio signals; Decoding; electropalatography; Feature extraction; Intelligibility; Loss measurement; model fusion; Noise measurement; Signal generation; Spectrogram; Speech; Speech enhancement; speech generation; Speech processing; Speech recognition; speech signal; Speech synthesis; Tongue; Verbal communication
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2022.3184636

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies of combining EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility of the generated speech signals. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. Finally, the late fusion strategy is deemed to be more effective for both speech generation and enhancement.