# Makedonka: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language
## 1 Introduction
The papers present a consecutive progress of DNN-based synthesizers, more precisely, Deep Voice 1 and 2 [21; 22] retain the traditional structure of TTS. They aim at separating grapheme-to-phoneme conversion, duration and frequency prediction, and waveform synthesis. Deep Voice 3 [23] is more complex character-to-spectrogram architecture, and it employs an attention-based sequence-to-sequence model. However, this approach proved to be most valuable for the successful creation of the Macedonian TTS system. Details can be found in Section 3.3. Researchers are not only satisfied with the accuracy of the models, but also of the time-performance and resources-creed of the methods. The newest proposals deal with the quality-training time trade-off. Therefore, in [24], the authors describe a TTS technique that is based on deep convolutional neural networks (CNN), but without any recurrent units, instead, stressing the importance of training the attention module. This means that they propose a solution that will reduce the need of very powerful computing resources and also reduce the time that is required to run the experiments, which usually takes several weeks. In the experiment, they have done comparative analysis of whether the CNN-based algortihm will produce acceptable quality of speech. They have trained the model in 15 h by using an ordinary gaming PC that is equipped with two GPUs, and the results proved the efficiency of the network in terms of synthesized speech quality. Some of the newest work is presented in [25]. In this work, the authors propose a new solution for parallel wave generation by WaveNet. The improved method in comparison to the previous work (Parallel WaveNet [26]), in which end-to-end speech synthesis actually refers to the text-to-spectrogrammodels with a separate waveform synthesizer (vocoder); this solution simplifies the training algorithm by introducing the first text-to-wave fully convolutional neural architecture for speech synthesis, which enables fast end-to-end training from scratch. Details regarding our experience in using this model architecture are discussed in Section 5. The key contributions of this paper are the following:  
* Development of the first open-source TTS model for Macedonian language. * Development of the first high-quality speech dataset for Macedonian language required for training TTS model, which consists of 10,433 short audio clips of a single female speaker. * Retraining an architecture of fully end-to-end TTS synthesis, from text records to inference for Macedonian language. * The development of a speech recorder tool for recording and matching the audio clips to the corresponding text transcriptions, suitable for easier creation of any language dataset needed for TTS model training. * Establishment of guidelines for other researchers via discussion of the experience using state-of-the-art Deep learning networks architectures for TTS modelling. The rest of the paper is organized, as follows. Section 2 presents the overall eminent work done in relation to creating Macedonian TTS system over the past 30 years. Section 3 describes the created dataset, data preprocessing, as well as the Deep Learning approaches experimented within the paper. Section 4 provides the results from the testing. Section 5 presents some valuable lessons learnt when trying to achieve the desirable human-like Macedonian TTS, by exploring many TTS models implementations. We believe that those leads might be very useful for other researchers and they are not usually found in the related papers. Section 6 presents the overall conclusions that are derived from the development of the TTS system. In addition, the future directions for the possibilities to integrate the Macedonian TTS system in almost any kind of e-services in North Macedonia are also presented in Section 6.