# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Methods Based on Deep Learning
#### 2.2.1 Lstm
The LSTM is trained using Backpropagation Through Time (BPTT), and the beam search solver selects the hypothesis sequence with the highest posterior probability as the decoding result, generating the phoneme sequence during the decoding phase. Although the encode-decode model does not require alignment, using alignment information to construct the model yields better results. The model structure is illustrated in Figure 5.  
The authors investigated alignment-based models, finding that unidirectional LSTM, unable to observe the entire input sequence, has a higher Word Error Rate (WER) with a default window size. The bidirectional LSTM (Bi-LSTM) model demonstrated strong performance, achieving a Phoneme Error Rate (PER) of 5.45% and a Word Error Rate (WER) of 23.55%. The model's prediction performance improved when it could observe the entire input sequence. In recent G2P models, alignment issues have not received much attention. Ref. [20] completely avoided alignment, while [21] simplified alignment to 1-to-1 or 1-to-2 cases. This did not maximize the performance of LSTM-based G2P models. Ref. [22] proposed an improved method for G2P models based on deep bidirectional Long Short-Term Memory (BLSTM) RNNs, utilizing complex many-to-many G2P alignments to enhance G2P model performance. They applied deep bidirectional LSTM with hyperparameter optimization, investigating models with varying numbers of hidden layers, linear projection layers, input concatenation windows, and different alignment constraints. The model was enhanced with  
Figure 4: Encoder–decoder LSTM. Figure 5: Alignment-based model. graphene-level LMs based on unidirectional LSTM, backoff models, and MAXENT models. Further improvements were achieved by decoding BLSTM outputs using a beam search mechanism with a phoneme-level LM. The best model, optimized with hyperparameters, achieved a Phoneme Error Rate (PER) of 5.37% and a Word Error Rate (WER) of 23.23%. Building on the encoder-decoder architecture developed by [21], ref. [23] used a multi-layer bidirectional encoder with Gated Recurrent Unit (GRU) nonlinearities and a similarly deep unidirectional GRU decoder, where each decoder layer's initial state was initialized to the corresponding encoder forward layer's final hidden state. The model was trained with teacher forcing and decoded using beam search, achieving 5.8% PER and 28.7% WER on CMUDict. Most G2P research uses pronunciation dictionaries as input, and training models to predict phonemes word by word, making it difficult to handle cross-word assimilation effects. Ref. [24] constructed a G2P converter based on deep neural networks, using short word sequences for training, which could easily handle (in a language-independent manner) inter-word and cross-word effects in phoneme sequences. The model was validated on three different languages (English, Russian, and Czech) and could easily be adapted with a set of transcription rules for irregular and regular pronunciation languages, competing with traditional dictionary and rule-based baseline methods in all tested languages. The model structure is illustrated in Figure 6.  
The model architecture is illustrated in the figure below: This model uses phrases as inputs to the network, converting all words \(W_{i}\) in the phrase into symbol sequences of equal length by using padding symbols. The first layer of the model is the embedding layer, which converts each symbol/graphene into a vector representation; and then embed it into a bidirectional LSTM layer to learn the phonetic relationships within words; the next LSTM encoder layer creates word embeddings to generate fixed-length representations for each word, with the sequence dimension changing from characters to words, and then passes these word embeddings to the next layer--biLSTM layer or convolutional layer; these layers learn the cross-word-boundary relationship; finally, an LSTM decoder is utilized to decode the fixed-length intermediate representation into phonemes.