# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 2 Previous Works  
G2P conversion has been studied for a long time. Rule-based G2P systems use a wide set of grapheme-to-phoneme rules [1; 2]. Developing such a G2P system requires linguistic expertise. Additionally, some languages (such as Chinese and Japanese) have complex writing systems, and building the rules is labor-intensive and it is extremely difficult to cover most possible situations. Furthermore, these systems are sensitive to out of vocabulary (OOV) events. Other previous solutions used joint sequence models [3; 4]. These models create an initial grapheme-phoneme sequence alignment, and by using this alignment, it calculates a joint n-gram language model over sequences. The method proposed by [3] is implemented in the publicly available tool Sequitur ([[https://www-i6.informatik.rwth-aachen.de/web/Software/g2p.html](https://www-i6.informatik.rwth-aachen.de/web/Software/g2p.html)]([https://www-i6.informatik.rwth-aachen.de/web/Software/g2p.html](https://www-i6.informatik.rwth-aachen.de/web/Software/g2p.html)), Access date: 9 th August 2018). In one-to-one alignment, each grapheme corresponds to only one phoneme, and vice versa. An \"empty\" symbol is introduced to match grapheme and phoneme sequences. For example, the grapheme sequence of 'CAKE' matches the phoneme sequence of 'K EY K', and one-to-one alignment of these sequences is C \(\rightarrow\) K, A \(\rightarrow\) EY, K \(\rightarrow\) K, and the last grapheme 'E' matches the \"empty\" symbol. Conditional and joint maximum entropy models use this approach [5]. Later, Hidden Conditional Random Field (HCRF) models were introduced in which the alignment between grapheme and phoneme sequence is modelled with hidden variables [6; 7]. The HCRF models usually lead to very competitive results; however, the training of such models is very memory and computationally intensive. A further approach utilizes conditional random fields (CRF) and Segmentation/Tagging models (such as linear finite-state automata or transducers, FSTs), then use them in two different compositions [8]. The first composition is a joint-multigram combined with CRF; the second one is a joint-multigram combined with Segmentation/Tagging. The first approach achieved 5.5% phoneme error rate (PER) on CMUDict. Recently, neural networks have been applied for G2P conversion. Neural network-based G2P conversion is robust against spelling mistakes and OOV words; it generalizes well. Also, it can be seamlessly integrated into end-to-end TTS/ASR systems (that are constructed entirely of deep neural networks) [9]. In this paper, a TTS system (Deep Voice) is presented which was constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. Thus, the G2P model is jointly trained with further essential parts of the speech synthesizer and recognizer, which increase the overall quality of the system. LSTM has shown competitive performance in various fields, like acoustic modelling [10] and language understanding [11]. One of the early neural approaches investigates unidirectional Long Short-Term Memory (ULSTM) with full output delays, which achieved 9.1% phoneme error rate [12]. In the same paper, a deep bidirectional LSTM (DBLSTM) was combined with connectionist temporal classification (CTC) and joint n-gram models for better accuracy (21.3% word error rate). Please note that CTC objective function was introduced to infer speech-label alignments automatically without any intermediate process, leading to an end-to-end approach for ASR [13]. CTC technique has combined with CNN, LSTM for the various speech-related tasks [14]. Due to utilizing an encoder-decoder approach for the G2P task, a separate alignment between grapheme sequences and phoneme sequences became unnecessary [15; 16]. Alignment-based models of unidirectional LSTM with one layer and bi-directional LSTM (Bi-LSTM) with one, two and three layers have also previously been investigated [16].