# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Methods Based on Deep Learning
#### 2.2.1 Lstm  
Long Short-Term Memory Networks (LSTM) have been successfully applied to tasks such as speech labels for acoustic frames, handwriting recognition, and language modeling. In acoustic frame labeling tasks, LSTM outperforms standard Recurrent Neural Networks (RNN). The grapheme-to-phoneme conversion task, which involves transcribing sequences from words to pronunciations, requires temporal context and is well-suited for LSTM network structures. Ref. [19] proposed a general end-to-end sequence learning method that utilizes Long Short-Term Memory networks (LSTM) to map input sequences to vectors of a specific dimension, and then uses another deep LSTM to decode the target sequence from this vector, eliminating the need for separate alignment between grapheme and phoneme sequences. Inspired by the model in [19], ref. [20] first applied LSTM to G2P conversion, investigating grapheme-to-phoneme models based on Long Short-Term Memory networks. They proposed three models: ULSTM, DBLSTM, and a combined model. The ULSTM model featured 1024 memory cells in the output layer, with a softmax activation function and cross-entropy loss function, initialized with random weights. Since ULSTM only utilizes left/past context, the authors introduced the concept of output delay and experimented with Zero-delay, Fixed-delay, and Full-delay scenarios. When using fixed delay, increasing the delay size slightly improved model performance. The Full-delay model outperformed any fixed delay model, achieving a 9.1% PER, demonstrating that larger context information can enhance model performance. The DBLSTM model is essentially the same as the unidirectional model but includes a \"backward\" LSTM layer, allowing it to see the entire input before producing the output phonemes. The structure is illustrated in the Figure 3. A single input layer is fully connected to two parallel layers, each with 512 units; one is unidirectional, and the other is bidirectional. The first hidden layer is fully connected to a unidirectional layer containing 128 units, which is then connected to the final output layer. The model is initialized with random weights and trained with a learning rate of 0.01. Connectionist Temporal Classification (CTC) is used as the output layer. The CTC output layer has a softmax output layer with 41 units, each corresponding to 40 output phoneme labels, plus an additional blank unit, where the probability of the blank unit indicates no label observed at a given time step. The performance of the BLSTM architecture outperforms that of the unidirectional LSTM and is roughly equivalent in accuracy to the joint n-gram model but with fewer parameters and faster speed. LSTM models are built at the full sequence (word) level, while joint n-gram models are built at the grapheme level. The authors also explored the performance of combining LSTM-based modeling methods with joint n-gram modeling  
Figure 3: DBLSTM-CTC Model. methods. The output of the LSTM model is represented as a Finite State Transducer (FST), which is then intersected with the FST output from the n-gram model, selecting a single best path in the final generated FST. This combined model achieved very good results on the CMU dataset, with a word error rate of 21.3%, demonstrating that combining the two modeling methods can produce a better overall model. The end-to-end sequence learning method proposed by [19] shows excellent performance. Due to the use of alignment, the method proposed by [8] achieved excellent conversion results. Inspired by this, ref. [21] employed alignment-based models in their research on G2P. The model structure is illustrated in Figure 4.  
The LSTM network structure is straightforward and can effectively capture the intricate relationships between letter sequences and phoneme sequences. One of its advantages is that it does not require explicit alignment information.