# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 2 Related Work  
The field of voice cloning has witnessed significant advancements in recent years, mainly driven by the remarkable progress in deep learning techniques. Numerous studies have explored various approaches and methodologies to tackle the challenges associated with this research field. In terms of applications of voice cloning, a prominent one is observed within the scope of deep faking [10; 11]. An illustrative instance is the interactive artwork released by the Salvador Dali Museum, featuring a deep fake representation of the renowned artist [12]. In terms of audio only, the AhoMyTTS project focuses on generating a collection of synthetic voices to aid individuals who are orally disabled or have lost their own voices [13]. Similarly, the Speech-to-Speech Parrotron [14] model serves the purpose of normalising atypical speech by converting it to the voice of a canonical speaker without speech disorders, thereby enhancing its intelligibility. Another successful voice cloning endeavour was demonstrated in the Euphonia Project [15], wherein the voice of a former American football player diagnosed with amyotrophic lateral sclerosis was recovered through the utilisation of a database containing his recordings [16]. Finally, the voice of former Spanish dictator [PERSON] was cloned for the emission of the XRey podcast [17], winner of the Best Podcast Ondas Award in the National Radio category, needed for the synthesis of a letter and an interview in a process explained in [3] by the authors. This research field can be broadly categorised into two main branches: voice conversion and speech synthesis or TTS. Voice conversion aims to transform the characteristics of a source speaker's voice into those of a target speaker, while preserving the linguistic content and speech quality of the original audio. In the past few years, the efficacy of deep learning techniques in voice conversion has been well-established. Various architectures including autoencoders have gained popularity for this purpose, such as variational autoencoders [18] and bottleneck-based autoencoders [19; 20]. Researchers have also explored the application of Generative Adversarial Networks (GANs) in this specific task [21; 22; 23]. Additionally, deep feature extractors have been leveraged to achieve successful outcomes. For instance, [PERSON] et al. [24] employed an approach based on HuBERT [25] for many-to-one voice conversion. On the other hand, deep learning techniques have emerged as the leading approach in the field of text-to-speech systems. WaveNet [2], which employs dilated Convolutional Neural Networks (CNNs) to directly generate waveforms, marked a significant milestone in this domain. Since then, numerous neural architectures have been developed by the scientific community. With Tacotron-2 [7], an attention-based system combined with a set of Long Short-Term Memory layers, many acoustic models that generate a Mel frequency spectrogram from linguistic input have been developed. Some of these architectures employ a phoneme length predictor instead of an attention matrix [26; 27; 28]. Additionally, sequence-to-sequence modelling has gained traction in this research field. [PERSON] et al. [29], for example, proposed the use of neural Hidden Markov Models (HMMs) with normalising flows as an acoustic model. In the context of generating a speech signal from Mel spectrograms, vocoders based on GANs [8; 30; 31; 32] have gained popularity due to their efficient inference speed, lightweight networks, and ability to produce high-quality waveforms. Furthermore, end-to-end models such as VITS [33] or YourTTS [34] have been developed, enabling the direct generation of audio signals from linguistic input without the need for an additional vocoder model. Finally, important advances have been made in terms of zero-shot TTS systems that feature voice conversion with the use of a decoder-only architecture. As an example, the system VALL-E is capable of cloning a voice with only 3 s of the target speaker [35].