# Makedonka: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language
## 1 Introduction  
Text-to-speech (TTS) is a challenging problem has attracted researchers' attention over the past 30 years. When considering the literature, it can be easily perceived that the level of success is, however, determined by the evolution of methods that overcome the human abilities of analysing and extracting features. Instead, those methods provide a higher-level abstraction of characteristics that fill the gap of producing human-like speech--something that was missing in the early efforts. Generally, a traditional TTS system is comprised of two consecutive main parts: text analysis and speech synthesis. The text analysis part includes text processing, or, morphological analysis and rule-based syntax analysis. The speech synthesis part encompasses methods that are able to recreate a speech by using a defined set of rules. The main problem with the traditional models for achieving the text-to-speech translation is that they rely on one-layer nonlinear transformation units [1]. Those are Hidden Markov models (HMMs) [2; 3; 4; 5], maximum Entropy based methods [6; 7; 8], and concatenation based synthesis methods [9; 10; 11]. It can be concluded that the existing attempts to create Macedonian TTS system, mostly rely on concatenation based synthesis methods, i.e., the synthesis is based on selecting an appropriate speech unit from a prerecorded and labeled speech corpus and adjusting the prosody of the concatenation unit according to the target context, as presented in the following Section 2. This approach quality is limited by the pitch period, or, starting point and the maintenance of smooth transitions [1]. In this paper, the focus is on creating the first Macedonian TTS system by following a parametric speech synthesis that is based on Deep Learning approach. The parametric speech synthesis has shown to significantly improve the naturalness of the generated speech [12; 13; 14]. Those approaches include three phases: text analysis, parameters prediction, and speech synthesis. All three steps have their traditional and DNN-based shape. The advantage of the DNN-based methods over the traditional ones is that they not only transform complex linguistic features into acoustic feature parameters, but also model the correlation between frames by using long short-term context information that improves the quality of the synthesized speech [1]. When compared to the HNN-based approach, in which the linguistic features are first mapped into probability densities, the DNN-based method directly maps the linguistic features to acoustic features. The text analysis itself can be done at few different levels: phoneme level, syllable level, word level (analysis of part of speech--POS), phrase level, and sentence level. Among the earliest attempts to use the leverage of DNN in the field of TTS is presented in [15]. The authors try to eliminate the decision tree clustering weakness (division of model space considering one feature at the time) in statistical parametric speech synthesis, by combining vector-space representations of linguistic context and DNNs. One of the most famous and widely used is Tacotron, which is a seq2 seq model [16], and its successor Tacotron2 [17; 18]. Given (text, audio) pairs, Tacotron is able to directly synthesize speech from characters. Tacotron has been an inspiration for some newer approaches that successfully overcome the speed problem, producing close, or, equal quality speech. Such is FastSpeech [19] and FastSpeech2 [20], the newest TTS model published at the time. However, both autoregressive models depend on additional models for duration prediction, which means that Tacotron2 model has to be trained to some satisfactory accuracy and, therefore, used for duration prediction needed for training FastSpeech2. Another option is using Montreal Forced Aligner (MFA) in order to obtain the alignments between the utterances and the phoneme sequences. Some valuable work done in the DNN-based speech synthesis field is presented in [21; 22; 23].