###### Abstract
Meanwhile, the pipeline approach obtaining semantic content from Mandarin EL speech through automatic speech recognition (ASR) [24] can also help improve the intelligibility and naturalness of Mandarin EL speech. Although the pipeline approach [25] effectively improves the intelligibility and naturalness of Mandarin EL speech, the approach includes multiple steps that may lead to hard-to-improve deficiencies such as high error rate of syllables or low tone accuracy. Therefore, both the parallel-dependent VC and the pipeline approach have their own drawbacks in dealing with Mandarin EL speech. [PERSON] et al. [26] proposed parallel-data-free VC based on cycle-consistent generative adversarial networks (CycleGAN). This CycleGAN-based VC does not require parallel source-target content but has poor performance in dealing with the Mel-Spectrogram [27]. The CycleGAN-VC3 (VC3 in this paper) proposed by [PERSON] et al. [27] incorporates a 2-1-2 dimension (2D-1D-2D) generator based on time-frequency adaptive normalization (TFAN), an improved version of CycleGAN-VC2 [28]. However, VC3 is still weak in processing Mandarin EL speech with complicated tone variations. To solve the problem, we design neural networks with 2D-conformer-1D-transformer-2D-transformer (2D-Con-1D-Tran-2D-Con) architecture as the generator for our CycleGAN, which is used to enhance Mandarin EL speech. The transformer proposed by [PERSON], et al. [29] and conformer proposed by [PERSON], et al. [30] achieve good performance in processing sequential data, and in particular, the transformer can capture long-distance dependencies. The conformer and transformer both have been successful in speech recognition and separation. The conformer in 2D-Con-1D-Tran-2D-Con architecture is used to process the input Mel-Spectrogram and the convolutional layer can capture both frequency and temporal information. The transformer in the proposed architecture can capture the dependencies of the input sequence. The proposed architecture is used to take advantage of both contextual semantic information and frequency information. WaveNet proposed by [PERSON] et al. [31] is an advanced speech synthesis technology. As a neural vocoder, WaveNet can synthesize high-quality speech based on low-dimension acoustic features such as the Mel-Spectrogram. WaveNet can effectively make up for the defects of traditional vocoders. In this paper, WaveNet is used to directly convert Mel-Spectrogram parameters into waveform signals. To our knowledge, we are the first to use CycleGAN-based VC to enhance Mandarin EL speech. The main contributions of this paper are as follows: The 2D-Con-1D-Tran-2D-Con neural networks are designed as the generator in CycleGAN to process the input, and the 2D-Conformer is designed as the discriminator in CycleGAN to discriminate whether the predictions approximate the outputs. This architecture is used to capture contextual semantic information to enhance the tone variation of continuous Mandarin EL speech. The CycleGAN-based VC is applied to address the problem that the source speech (Mandarin EL speech) and the target speech (normal speech) are not strictly parallel. This is because the Mandarin EL speech has some tone errors. Objective and subjective evaluations are designed to test the proposed approach. In particular, average tone accuracy and word perception error rate are used in subjective evaluation to explain the effect of enhancement. The rest of this paper is organized as follows. Section 2 introduces the proposed methods to further enhance Mandarin EL speech. Section 3 introduces the experimental results of evaluation for enhancement. Section 4 discusses the advantages and limitations of the proposed methods. Section 5 give the conclusions of our research.