# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 2 Previous Works
In this work, alignment was explicitly modelled in the G2P conversion process by the context of the grapheme. A further work, which applies deep bi-directional LSTM with hyperparameter optimization (including the number of hidden layers, optional linear projection layers, optional splicing window at the input) considered various alignment schemes [17]. The best model with hyperparameter optimization achieved a 5.37% phoneme error rate (PER) and a 23.23% word error rate (WER). Multi-layer bidirectional encoder with gated recurrent units (GRU) and deep unidirectional GRU as a decoder achieved 5.8% PER and 28.7% WER on CMUDict [9]. Convolutional neural networks have achieved superior performance compared to previous methods in large-scale image recognition [18; 19]. Recently, these architectures were also applied to Natural Language Processing (NLP) tasks, including sentence classifications and neural machine translation. Nowadays, completely convolutional neural networks may achieve superior results compared to recurrent solutions [20; 21]. Sequence-to-sequence (seq2 seq) learning, or encoder-decoder type neural networks have achieved remarkable success in various tasks, such as speech recognition, text-to-speech synthesis, machine translation [22; 23; 24; 25]. This type of network is used for several tasks, and its performance has also been enhanced with attention mechanisms [21; 25; 26; 27; 28]. In this structure, the encoder computes a representation of each input sequence, and the decoder generates an output sequence based on the learned representation. In [28], bidirectional multi-layer recurrent neural network-based seq2 seq learning was investigated in two architectures: a single Bi-LSTM/Bidirectional Gated Recurrent Unit (Bi-GRU) layer and two Bi-LSTM/Bi-GRU layers. Both Bi-LSTM and Bi-GRU uses both past and future contexts. Moreover, a bidirectional decoder was proposed for neural machine translation (NMT) in [29]. Both encoder and decoder are Bi-GRU, but this model is applicable to other RNNs, such as LSTM. By introducing a backward decoder, the purpose of which is to exploit reverse target-side contexts, the results of NMT task was improved. For speech recognition, several sequence-to-sequence models, including connectionist temporal classification (CTC), the recurrent neural network (RNN) transducer, and an attention-based model [30], have been analyzed. The basics of sequence modelling with convolutional networks are summarized in [31]. Furthermore, the key components of the temporal convolution network (TCN) have also been introduced, and some vital advantages and disadvantages of using TCN for sequence predictions instead of RNNs were analyzed as well. The encoder-decoder structure has been studied for the G2P task [9; 16; 32] before, but usually, LSTM and GRU networks have been involved. For example, Baidu's end-to-end text-to-speech synthesizer, called Deep Voice, uses the multi-layer bidirectional encoder with GRU's non-linearity and an equally deep unidirectional GRU decoder [9]. Until now, the best result for G2P conversion was introduced by [32], which applied an attention-enabled encoder-decoder model and achieved 4.69% PER and 20.24% WER on CMUDict. Furthermore, G2P-seq2 seq ([[https://github.com/cmusphinx/g2p-seq2](https://github.com/cmusphinx/g2p-seq2) seq]([https://github.com/cmusphinx/g2p-seq2](https://github.com/cmusphinx/g2p-seq2) seq), Access date: 9 th August 2018) is based on neural networks implemented in the TensorFlow framework with 20.6% WER. To the best of our knowledge, our approach is the first that uses convolutional neural networks for G2P conversion. In this paper, we present one general sequence-to-sequence and four encoder-decoder models. These are introduced in Section 3. Our goal was to achieve and surpass (if possible) the accuracy of previous models and to reduce the training times (which is quite high in the case of LSTM/GRU).