# Evaluation of Tacotron Based Synthesizers for Spanish and Basque
## 1 Introduction  
The aim of Text to Speech (TTS) systems is to create synthetic speech from input written language. In recent years, very rapid progress has been made in this technology, improving the quality of the synthetic voices and increasing its use in consumer applications such as virtual agents, automated call assistance and many others. Traditionally, two approaches have been used to produce synthetic voices: unit selection (US) based concatenative synthesis [1, 2] and statistical parametric speech synthesis (SPSS) based on Hidden Markov Models (HMM) [3, 4]. These approaches are based on complex multi-stage pipelines and require from large domain expertise, impeding wider access to this technology. Nowadays, Deep Neural Network (DNN)-based systems are state-of-the-art in speech synthesis [5, 6]. DNNs provide TTS systems with the capacity to capture in a more efficient way the nonlinear complex relations between the voice acoustic parameters and the symbolic representation of speech. This improvement results in the generation of higher quality and more natural speech than the one obtained by traditional methods. Some examples of architectures based on DNNs are the Feed-Forward Networks (FF) [7], Recurrent Neural Network (RNN) [6] and the WaveNets [8]. Over the last few years, the implementation of neural networks has been used as an alternative approach to complex traditional multi-stage pipelines. Current systems are predominantly trained and designed in an end-to-end (E2E) fashion, meaning that audio is directly extracted from the input text without using separated models. E2E systems introduce three main advantages: (i) They prevent the propagation of errors through different components; (ii) they lower the need for feature engineering; and (iii) they reduce the requirement of manual annotations, minimizing costs. Different neural network based E2E architectures have been proposed in the deployment of TTS synthesizers. Deep Voice [9] was the first proposed E2E system. Thequality obtained by this model was not as high as the quality obtained by previous models, therefore several improvements were developed in the subsequent versions Deep Voice 2 [10] and Deep Voice 3 [11]. Another example of a system built in an E2E fashion is Char2 Wav [12], which was able to produce audio directly from text but revealed some artefacts in the speech signal. All the TTS synthesizers implemented in this work were developed using Tacotron 2 [13] as a basis. Tacotron 2 is a system that addresses the sequence-to-sequence learning problem by means of an architecture inspired on the proposal by [PERSON] et al. [14]. In Tacotron 2, a sequence of input character embeddings is mapped to a sequence of mel-scale spectrogram frames. To transform the spectrograms into waveforms, we used a neural vocoder named WaveGlow [15]. WaveGlow is a network that combines insights of Glow [16] and WaveNet [8] to produce high-quality audio at a fast speed thanks to its parallel processing capabilities. Although E2E TTS systems show excellent results in terms of quality and naturalness of the generated synthetic speech, there are two main issues that must be addressed. Firstly, these systems require a sizeable set of audio and text pairs to be properly trained [17]. The strong dependence these system have on large amounts of data make them less accessible than traditional synthesis techniques, especially for low-resourced languages. Secondly, these models suffer from alignment issues, specially in long utterances. Alignment failures between the input text and the generated speech lead to unintelligible or incomplete signals. The aim of this work is to evaluate the performance of Tacotron 2 based systems when trained on limited amounts of speech data. With this purpose in mind, we developed different TTS systems for Spanish and Basque. The main contribution of this research is the analysis of the performance of these systems on robustness, quality and naturalness.