# Assessment of Self-Supervised Denoising Methods for
## 1 Introduction  
Certainly, speech serves as the primary means of communication for humans. However, people suffering from pathological voice disorders encounter obstacles in both communication and social interaction, which have a considerable impact on their overall quality of life [1; 2; 3]. One of the multiple causes of voice dysfunctions is laryngeal or hypo-pharyngeal cancer [4]. In the advanced stages of this disease, the larynx needs to be removed entirely [5; 6; 7], which is a crucial apparatus for speech generation using vocal folds [4; 8; 9]. However, with the help of a speech therapist [8; 9; 10], patients can communicate through either esophageal speech (ES), tracheo-esophageal speech (TES), or electro-larynx speech (EL Speech) [1; 3; 4]. ES is the most commonly used communication tool and sounds more natural than EL Speech [6]. Still, it is often characterized by harsh speech with specific noises, low pitch range and intensity [11; 12], weak intelligibility, and being difficult to understand [13]. Therefore, this study aims to find suitable methods to enhance ES and retain the speaker's identity so that laryngectomy patients can have a better quality of life. Since obtaining recordings of laryngectomy patients before the ablation operation is challenging, most researchers used voice conversion (VC) technology in their studies to enhance esophageal speech [2; 3; 4; 6; 11; 12; 14]. VC consists of manipulating the speech characteristics of the ES source speaker to replicate the vocal qualities of another healthy target speaker without altering the linguistic information, meaning, or content [15]. Many studies have been conducted to investigate this. An approach to increase the clarity and naturalness of ES using a statistical voice Esophageal-Speech-to-Speech conversion technique has been proposed in [6]. The objective of this method was to retain the linguistic content while changing esophageal speech into speech that sounds natural by applying probabilistic approaches. Another study proposed a voice conversion system based on Gaussian mixture models (GMMs) and deep neural networks (DNNs) to enhance the quality of ES [14]. This research aimed to lower speech noise and maintain the unique features of the esophageal utterer using the time-dilated Fourier cepstra on the source vocal tract. To accomplish this objective, GMMs were applied as the primary voice conversion technique, and the deep neural network was trained to function as a nonlinear mapping function. In addition, the study used a frame selection algorithm to predict the excitation and phase separately. The research conducted in [11] utilized a neuromimetic statistical method to enhance the naturalness and lucidity of ES. This method involved estimating the vocal tract and excitation cepstral coefficients independently. DNN model and GMMs were trained with the original cepstral vocal tract, while the phase and cepstral excitation were estimated by tracing the target training space through a KD-tree. The system proposed in [12] used an attention mechanism specifically for esophageal-to-laryngeal voice conversion by incorporating adaptive mapping between esophageal and healthy features. Also, the speaker's identity was preserved by determining the excitation and phase coefficients from a target-learning space, organized as a binary search tree, and queried using the vocal tract coefficients predicted by the Seq2 Seq model. As per another research paper [4], a phase-based approach was introduced to improve ES. The method involves aligning the source and target phases using the Fast Dynamic Time Warping method (FastDTW) and then using the best alignment path as input to the deep neural network. Additionally, this technique preserves the identity of the source speaker by incorporating the cepstral coefficients of their vocal tract in the reconstruction of the improved sound file. Similar to supervised learning, voice conversion models require copious amounts of data and a substantial quantity of labeled data to achieve high performance.