# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
## 1 Introduction
They have also been used in end-to-end TTS systems, such as the aforementioned DeepVoice [12], as conditioning to processors to upsample linguistic and prosodic features to the waveform resolution used by a WaveNet generator. Another system is the Transformer network, which also conceptualizes sequential dependencies differently from RNNs, avoiding any sort of recurrent connection. The Transformer network was designed as a sequence-to-sequence model for machine translation, where typically RNNs are applied to deal with the conversion between the two sequences [31; 32]. In [33], [PERSON] et al. specifically introduced the self-attention mechanism, which can relate elements within a single sequence regardless of the sequential order by using a compatibility function. Nonetheless, the order awareness is important, thus, in this system, it is imposed with positioning codes that serve as time-stamps (so input features rather than neural connections). In this article, which continues from our previous work [34], we investigate the performance in terms of both prediction efficiency and voice quality of three types of sequential processing architectures for linguistic-acoustic mapping in two-stage TTS systems. Firstly, we depart from an RNN reference model that uses LSTM cells, as proposed in previous works [7; 8]. Secondly, we use a QRNN-based model named QRNN linguistic-acoustic decoder (QLAD), based on the previous mentioned evidence of more efficient computations than LSTM structures, albeit being comparable in performance across different sequential tasks. QRNNs have been used in the conditioning branch of end-to-end TTS, as mentioned above, but we instead propose them to actually generate the acoustic parameter trajectories of our decoder. We also consider a Transformer encoder, thus discarding its original attention decoder structure, because we are dealing with a mapping between two sequences that have the same time resolution. We however call this part the decoder in our case, given that we are decoding linguistic contents into their acoustic codes. Precisely a key difference between our Transformer module and the recently proposed Transformer TTS is that we do not work with the full Transformer structure as other end-to-end solutions do [17]. We thus call this system the self-attention linguistic acoustic-decoder (SALAD) [34]. Acoustic models that improve the speech synthesis efficiency over the existing RNN-based ones are a suitable target for low resource environments as long as the generated speech quality is not degraded. This specially fits embedded devices, such as mobile phones, with which the TTS system can then run locally. Furthermore, the experimentation carried out in this work is applicable to further settings where text is not the input signal but speech is the synthesized output (e.g., speech enhancement or voice conversion [35; 36], where speech signals are converted into other speech signals). Moreover, some of these architectural changes can also be extended for more end-to-end waveform generation solutions similar to those of WaveNet [2] or WaveRNN [3]. Nonetheless, our focus is on exploring these variations on the vocoder acoustic parameter generation for ease of experimentation, both in computational requirements and given the amount of training data available to succeed in the level of synthesized naturalness. We empirically found that both QLAD and SALAD models are competitive in terms of efficiency for training and inference. Additionally, both models show competitive objective results against those of the RNN. Nonetheless, QLAD models are the only ones that subjectively match, or even improve in some setting, the voice naturalness of RNNs. This paper is structured as follows. In Section 2, we introduce the linguistic-acoustic decoding framework we worked with, based on an RNN acoustic model. We then describe the QLAD and SALAD models in Sections 3 and 4.