# A Survey of Graphene-to-Phoneme Conversion Methods
## 5 Dataset and Evaluation Metrics
### Comparison of Different G2P Models  
Here are two tables of the performance of G2P models summarized from mentioned research papers. Table 1 compares the results of the various current G2P conversion algorithms on the CMUdict dataset and NetTalk Dataset; all the G2p models in the table are designed as English monolingual conversion. Table 2 illustrates the multilingual G2P conversions on different datasets. Since there is no general multilingual grapheme-to-phoneme database, all the datasets for the multilingual G2P models are collected or designed by the researchers, so the performance may vary with the change of dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**CMUdict Dataset**} & \multicolumn{2}{c}{**NetTalk Dataset**} \\ \hline
**Method** & \(PER\) (\%) & \(WER\) (\%) & \(PER\) (\%) & \(WER\) (\%) \\ \hline Joint sequence model [8] & 5.88 & 24.53 & 8.26 & 33.67 \\ \hline Joint maximum entropy (ME) n-gram model [62] & 5.9 & 24.7 & & \\ \hline Bi-LSTM + Alignment [21] & 5.45 & 23.55 & 7.38 & 30.77 \\ \hline Uni-directional LSTM [21] & 8.22 & 32.64 & & \\ \hline Encoder–decoder LSTM (2 layers) [21] & 7.63 & 28.61 & & \\ \hline Many-to-many alignments with deep BLSTM RNNs [22] & 5.37 & 23.23 & & \\ \hline Failure transitions for joint n-gram models and g2p conversion [11] & 8.24 & 33.55 & 5.85 & 24.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of G2P conversion algorithms on the CMUdict dataset and NetTalk dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**CMUdict Dataset**} & \multicolumn{2}{c}{**NetTalk Dataset**} \\ \hline
**Method** & _PER (\%)_ & _WER (\%)_ & _PER (\%)_ & _WER (\%)_ \\ \hline Joint n-gram model [63] & 7.0 & 28.5 & & \\ \hline End-to-end CNN (with res. connections) & 5.84 & 29.74 & & \\ (model4) [27] & 4.81 & 25.13 & 5.69 & 30.1 \\ \hline Encoder CNN, decoder LSTM with attention layer & 5.23 & 28.36 & & \\ (model1) [27] & 5.23 & 28.36 & & \\ \hline LSTM with Full-delay [20] & 9.1 & 30.1 & & \\ \hline DBLSTM-CTC 512 Units [20] & & 25.8 & & \\ \hline
8-gram FST [20] & & 26.5 & & \\ \hline DBLSTM-CTC 512 + 5-gram FST [20] & & 21.3 & & \\ \hline Joint multi-gram + CRF [18] & 5.5 & 23.4 & & \\ \hline Combination of sequitur G2P and seq2 seq-attention and multitask learning [38] & 5.76 & 24.88 & & \\ \hline Encoder–decoder with global attention [28] & 5.04 & 21.69 & 7.14 & 29.2 \\ \hline Encoder–Decoder GRU [23] & 5.8 & 28.7 & & \\ \hline Transformer 4x4 [31] & 5.23 & 22.1 & 6.87 & 29.82 \\ \hline CNN with NSGD [26] & 5.58 & 24.1 & 6.78 & 28.45 \\ \hline LiteG2P-medium [25] & & 24.3 & & \\ \hline r-G2P (adv) [64] & 5.22 & 20.14 & 6.64 & 28.85 \\ \hline MTL (512 \(\times\) 3, \(\lambda\) = 0.2) [65] & 5.26 & 22.96 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dataset Description** & _PER (\%)_ & _WER (\%)_ \\ \hline Ensemble model [41] & 15 languages from Wikionary & 14.83 & 3.41 \\ \hline Encoder decoder multilingual model [66] & Chinese, Tibetan, English, and Korean, totaling 9620 words. & & 6.02 \\ \hline Multitask Sequence-to-Sequence Models [38] & German from Phonolex, English from CMUDICT dataset & 3.73 & 17.2 \\ \hline Multimodal and Multilingual model [40] & 20 languages from the CMU & 26 & 37.87 \\ \hline Multilingual neural G2P model [50] & English, French, Spanish, Japanese, Chinese, total 10 million pairs & 4.03 & 16.14 \\ \hline LangID-All [46] & Test set 507 languages, training set 311 languages & 37.85 & 7.41 \\ \hline DialectalTransformerG2P [43] &
\begin{tabular}{c} Different dialects of English, including American English, Indian English, and British \\ English. \\ \end{tabular} & 1.457 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of G2P conversion algorithms on the multilingual datasets.