This paper presents a novel approach for enhancing esophageal speech using voice conversion techniques. Esophageal speech (ES) is an alternative voice that allows a patient with no vocal cords to produce sounds after total laryngectomy: this voice has a poor degree of intelligibility and a poor quality. To address this issue, we propose a speaking-aid system enhancing ES in order to clarify and make it more natural. Given the specificity of ES, in this study we propose to apply a new voice conversion technique taking into account the particularity of the pathological vocal apparatus. We trained deep neural networks (DNNs) and Gaussian mixture models (GMMs) to predict " laryngeal " vocal tract features from esophageal speech. The converted vectors are then used to estimate the excitation cepstral coefficients and phase by a search in the target training space previously encoded as a binary tree. The voice resynthesized sounds like a laryngeal voice i.e., is more natural than the original ES, with an effective reconstruction of the prosodic information while retaining , and this is the highlight of our study, the characteristics of the vocal tract inherent to the source speaker. The results of voice conversion evaluated using objective and subjective experiments , validate the proposed approach.