# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 5 Discussion
We also analyzed the position of the errors in the reference pronunciation: we investigated whether the error occurred in the first or in the second half of the word. The type of error can be insertion (I), deletion (D) and substitution (S). By using this position information, we can analyze the distribution of these errors across the first or second half of the word. The position of error was calculated by enumerating graphemes in the reference. For insertion error (I), the position of the previous grapheme was taken into account. The example below describes the process details:  
Word: Acknowledgement  
Enumeration: 0 1 2 3 4 5 6 7 8 9 10 11 12  
Reference: [EP AE K N AA L IH JH M AH N T /EP]  
Prediction: [EP IH K N AA L IH JH JH JH AH N T /EP]  
Types of errors: S S I  
Position: [1,8,8]  
As the example shows, two substitutions (S) and one insertion (I) occurred in our fifth model output. One error (S) is included in the first half part of the pronunciation in the reference (EP AE K N AA L, the other errors (S) and (I) are in the second half (H JH M AH N T/EP). Figure 6b shows the position errors calculated for all the models on the reference dataset. The first half of the words in all models contains more errors. Regarding the second half, all models show a similar number of position errors, except the end-to-end CNN model. The fifth model resulted in the lowest number of position errors. Figure 6: PER depending on the length of the words (**a**); position of errors for all models (**b**). Furthermore, in all models presented here, PER is better than the previous results on CMUDict except the first four models in [32], while WER is still reasonable. This means that even most of the incorrect predictions are very close to the reference; therefore, they have small PER. Accordingly, we need to analyze the incorrect predictions (outputs) for each model to see how many phonemes are correct in the reference. In the fifth model, 25.3% of the test data are not correct (about 3000 test samples). After the analysis of these predictions, more than half of them have 1 incorrect phoneme. In particular, the PER for 59 test samples is higher than 50% (11 test samples are greater than 60%, and only 1 test sample is more than 70%). These percentages in the other presented models are more or less the same. Generally, the same 1000 words are incorrectly predicted by all presented models. We can see different types of error when generating phoneme sequences. One of these errors is that some phonemes are unnecessarily generated multiple times. For example, for the word YELLOWKNIFE, reference is [Y EH L OWN AY F], the prediction of Model 5 for this word is [Y EH L OW K N N F], where the character N was generated twice. Another error type regards sequences of graphemes that are rarely represented in the training process. For example, for the word ZANGHI Model 5 output is [Z AE N G], while the reference is [Z AA N G LY]. The graphemes 'NGHI' appeared only 7 times in the training data. Furthermore, many words are of foreign origin, for example, GDANSK is Polish a city, SCICCHITANO is an Italian name, KOVACIK is a Turkish surname. Generating phoneme sequences of abbreviations is one of the hard challenges. For example, LPN, INES are shown with their references and the prediction form of Model 5 in Table 3:  
In the proposed models, we were able to achieve smaller PERs with different hyperparameter settings, but WERs showed different behavior, in contrast with what we expected. To calculate WER, the number of word errors is divided by the total number of unique words in the reference. These word errors are counted only if the predicted pronunciation does not match any reference pronunciation. Therefore, in the generated phoneme sequences of words that contained errors, there is at least one phoneme error.