## 2 Related Work  
Non-parallel VC techniques are even more challenging because they do not need parallel data for training. Some successful non-parallel VC methods include variational autoencoder (VAE) [21; 27], generative adversarial network (GAN) [22] and its variants such as CycleGAN [17] and StarGAN [18]. Although these methods have focused on transforming a non-parallel corpus into a quasi-parallel corpus and then on learning a conversion function (which is not so straightforward), they can lead to a degradation of speech quality. Recent methods based on the use of linguistic features PPGs and vocoders have also been proposed and have proven to be effective [10; 23; 24; 25; 26; 28]. PPGs are high-level contextual representations obtained from the posterior probabilities of each phonetic class using a speaker-independent ASR system. Although PPG techniques have been applied successfully, they still have inherent limitations, e.g., the quality of the PPGs is highly dependent on the ASR system. While conventional parametric vocoders, as mentioned in the work of [PERSON] et al. [29], could be utilized, they tend to produce synthesized speech of lower quality than neural vocoders. In particular, WaveNet, presented by [PERSON] et al. [24], represents a very successful implementation of a neural vocoder. WaveNet operates as an autoregressive generative model, known for its ability to generate high-fidelity audio waveforms. WaveNet's autoregressive structure greatly improves the continuity of the generated waveforms; however, its drawback lies in the slowness of real-time synthesis, due to the one-by-one generation of waveform samples. In response to this limitation, an alternative to WaveNet has been proposed in the form of WaveRNN [30], which seeks to improve the quality of the WaveNet model. WaveRNN uses a layer of sparse gated recurrent units (GRUs) rather than the dilated causal convolutions used in WaveNet. Recently, a highly efficient neural vocoder, known as LPCNet [31], has been introduced, drawing inspiration from WaveRNN. LPCNet leverages the principles of linear predictive coding (LPC) to model vocal tract responses and incorporates linear prediction techniques into the WaveRNN architecture, resulting in a reduction in the complexity of generating raw speech waveforms. Notably, LPCNet achieves the synthesis of higher-quality speech compared to WaveRNN, even when using the same network size. Moreover, LPCNet exhibits real-time or faster-than-real-time performance on a single CPU core, thanks to efficient vectorization techniques. Since its inception, LPCNet has emerged as a popular choice for various speech synthesis tasks. Hence, numerous approaches have been proposed to improve the inference speed of LPCNet [32; 33; 34]. In addition, there is considerable enthusiasm for high-fidelity neural vocoders that exploit generative adversarial networks for their lightweight architectures and fast speech generation capabilities [35; 36; 37]. Nevertheless, the training of these vocoders can pose challenges, potentially leading to audible artifacts like pitch errors and periodicity artifacts, attributed to their non-autoregressive (non-AR) structures [38]. Our proposed method differs from conventional PPG-VC techniques, such as the works of [PERSON] et al. [23] and [PERSON] et al. [39], in that we use the acoustic features of the previous step as input to generate next-step output through the proposed AR structure, resulting in a smooth waveform and low speech distortion. Indeed, taking advantage of the PPGs, our proposal allows for any-to-one conversion due to its speaker-independent characteristics. In addition, as part of our approach, we use speaker embeddings derived from a speaker encoder network, originally trained for the classification of multiple speakers [40], as additional features to more accurately capture the characteristics of target speakers. The concatenation of PPGs and speaker embedding features results in a more intelligible converted speech.