Grapheme to phoneme (G2P) conversion is an integral part in various text and speech processing systems, such as: Text to Speech system, Speech Recognition system, etc. The existing methodologies for G2P conversion in Bangla language are mostly rule-based. However, data-driven approaches have proved their superiority over rule-based approaches for large-scale G2P conversion in other languages, such as: English, German, etc. As the performance of data-driven approaches for G2P conversion depend largely on pronunciation lexicon on which the system is trained, in this paper, we investigate on developing an improved training lexicon by identifying and categorizing the critical cases in Bangla language and include those critical cases in training lexicon for developing a robust G2P conversion system in Bangla language. Additionally, we have incorporated nasal vowels in our proposed phoneme list. Our methodology outperforms other state-of-the-art approaches for G2P conversion in Bangla language. Grapheme to phoneme (G2P) conversion provides a mapping between a word and its pronunciation. Such mapping provides opportunity for a nonnative person to learn the correct pronunciation of words of a foreign language. Moreover, in modern Text to Speech (TTS) and Automatic Speech Recognition (ASR) systems, G2P conversion is an integral task. The task of G2P conversion is generally language specific due to language specific conventions, rules, pronunciation constraints, etc. In this paper, we focus on Modern Standard Bangla. An example of G2P conversion in Bangla language: phonetic transcription of  (practice) is /o n u sh i l O n/. (Please refer to Table 1 for our phoneme symbols.) The simplest means of G2P conversion is to build up a lexicon or dictionary containing the mapping from words to their corresponding pronunciations. However, it fails to provide pronunciations for unknown words and inclusion of newer words increases memory requirement. In another approach (Mosaddeque et al., 2006), there are predefined rules for the conversion of a word to its pronunciation. Though such rule-based approach can work for any word, the system becomes complex when it tries to formulate rules for incorporating all irregularities of pronunciation in a language. Clearly, these approaches are not feasible for large-scale G2P conversion which is necessary in any modern TTS or ASR system. Data-driven machine learning approaches have great potential in such large-scale G2P conversion (Rao et al., 2015). In such an approach, a machine learning model predicts the phoneme conversion of a grapheme, being trained on a lexicon. A predominant work following such approach in Bangla language is by Google (Gutkin et al., 2016), where they train their system using 37 K words and achieve word-level accuracy of 81 : 5 % . However, a system trained on their lexicon will face several shortcomings, such as:  (mud) and  (to cry) are pronounced differently but will have same phoneme representation in their system as: /k a d a/. Similarly,  (fairy) and (cid:278) (to read) are pronounced differently but will have same phoneme representation in their system as: /p o r i/. Moreover, G2P system trained on their lexicon performs poorly on our identified critical cases from the most frequent 100 K words (Table 3). Being motivated to increase the accuracy of grapheme to phoneme conversion in Bangla language, which will also perform well for critical inputs, we have developed a customized and robust G2P system for Bangla language. Our major contributions are as follows: (i) We identify and categorize the critical cases for grapheme to phoneme (G2P) conversion in Bangla language by analyzing the most frequent 100 K words. (ii) We enrich the training lexicon for developing a robust G2P conversion system in Bangla language that performs much better for critical cases compared to other state-of-the-art G2P systems. (iii) We perform phonetic transcriptions considering nasal vowels as separate phonemes. (iv) We perform extensive simulations on large-scale dataset and show that our methodology outperforms other state-of-the-art approaches for G2P conversion in Bangla language by providing word-level accuracy of 90 : 2 % . The rest of the paper is organized as follows: we discuss the previous works in Section 2, our phoneme list in Section 3, identification of critical cases and categorization of errors in Section 4, development of our system in Section 5, experimental results in Section 6, and conclusion and future works in Section 7. The research works for G2P in English are quite extensive. Chen (2003) investigate machine learning based systems for G2P in English. They experiment with joint maximum entropy n-gram model, conditional maximum entropy model, etc. Yao and Zweig (2015) utilize bi-directional LSTM (Long Short Term Memory) recurrent neural network for G2P and achieve 5 : 45 % PER on CMU dictionary (air, 2015). Thu et al. (2016) show comparisons among various machine learning algorithms for G2P in Burmese language. Joint sequence n-gram models aim to discover joint vocabulary consisting of graphemes and phonemes through the alignment of graphemes and phonemes. Bisani and Ney (2008) develop a joint-sequence model for G2P. Novak (2012), Novak et al. (2013) are other prominent works working on this model. Neural sequence to sequence models are popular for G2P conversion. Some prominent works on such models are: Caruana (1997), Jiampojamarn et al. (2007), Sutskever et al. (2014), Yao and Zweig (2015), Jiampojamarn et al. (2007), Rao et al. (2015), Yao and Zweig (2015), Schnober et al. (2016), Tsvetkov et al. (2016), He et al. (2016), Wu et al. (2016), Johnson et al. (2016), Toshniwal and Livescu (2016), and Vaswani et al. (2017). Again, another line of research deals with G2P conversion for more than one language. Such works include: Mana et al. (2001), Kim and Snyder (2012), Deri and Knight (2016), and Milde et al. (2017). Most of the works related to G2P conversion that are focused on Bangla language, follow rule-based approach. Rule-based approach of Mosad-deque et al. (2006) provides accuracy of 97 : 01 % on a previously seen corpus containing 736 words, but the system's accuracy is 81 : 95 % on an previously unobserved corpus containing 8399 words. This work was extended by Alam et al. (2011) describing 3880 rules with an accuracy of 89 : 48 % on another corpus. Basu et al. (2009) discuss a rule-based approach considering several information: parts-of-speech, subsequent context, etc. Their work describes only 21 rules and provides an accuracy of 91 : 48 % on a corpus of 9294 words. Ghosh et al. (2010) provide a heuristic for G2P that takes into account parts-of-speech, orthographic, and contextual information. Their work provides 70 % accuracy on a corpus containing of 755 words. A prominent work for data driven G2P in Bangla language is by Google (Gutkin et al. (2016)). They develop a lexicon and achieve word-level accuracy of 81 : 5 % . Chowdhury et al. (2017) use conditional random field for G2P in Bangla. They report 14 : 88 % phoneme error rate on Google lexicon. Our Phoneme symbols are provided in Table 1. This table is a good reference for the 47 phoneme symbols that we have followed in this paper and their corresponding International Phonetic Alphabet (IPA) symbols. Throughout the paper, we use these 47 phoneme symbols, not the IPA symbols. There is disagreement between linguists whether nasal vowels should be considered as separate phonemes (Barman, 2009). We added nasal vowels in our phoneme list to differentiate between a word with its nasalized counterpart, such as the word  (to cry) and  (mud). Here, /a/, /e/, /u/, /i/, /o/, /O/, /E/, /an/, /en/, /un/, /in/, /on/, /On/, /En/ are normal vowels, /ew/, /ow/, /uw/, /iw/ are weak vowels, and the rest are consonants. We envision of developing a robust G2P system that will perform reasonably well on any word in Bangla language. A G2P system that performs well on the most frequent words, should also do well on other words. With this motivation, we focus on increasing accuracy on the most frequent words. Especially, we are concerned about those words that are among the most frequent words but non-trivial or critical for phonetic transcription, i.e., current state-of-the-art methodologies perform poorly on these critical words. We investigate on identifying and categorizing such nontrivial or critical cases so that future research works can give special focus on developing methods for improving phonetic transcriptions of these critical words. To get a hold of contemporary usage of Bangla language, we do extensive crawling. We crawled 42 websites of various Bangla newspapers, blogs, e-book libraries, wikipedia, etc. covering various domains such as: politics, economics, sports, drama, novel, story, education, entertainment, general knowledge, history, etc. After data cleaning and data normalization, we had about 10 M sentences. We counted how many times each of the unique words appeared in those sentences. We then consider the most frequent 100 K words and aim to identify the critical cases for phonetic transcription among these most frequent words. Transcription After changing the Google lexicon (of size 60 K (around)) according to our phoneme symbols (Ta-ble 1), we prepare 4 versions of Google's lexicon of size 12 K , 24 K , 40 K , and 60 K respectively for identifying the critical cases for phonetic transcription. Algorithm 1 shows prefix comparing algorithm that we use for compressing a phonetic lexicon or dictionary of grapheme sequence to phoneme sequence. The algorithm matches the prefix of consecutive words (grapheme sequence) of a sorted dictionary (sorted according to ascending order of grapheme sequence of a word) and keeps a word (with its corresponding phoneme sequence) only if it does not share its prefix with any other words. We run the algorithm successively 3 times, i.e., we use the destination dictionary of one iteration as the source dictionary of next iteration. Each iteration produces a minimized version of the basic lexicon (Google lexicon). After 3 iterations, the dictionary does not get any more compressed. We find the phonetic transcriptions of each of the 100 K most frequent words using models trained on each of the 4 versions of Google's lexicon (ba-sic + 3 minimized). So, from 4 models (each model trained on a version of the basic Google lexicon), we get 4 sets of transcriptions for the most frequent 100 K words. For most of the words (around 70 K words), we observe that the phonetic transcriptions are exactly same in each of the 4 set. However, for the remaining 30 K words ( 29105 words to be ex-act), we observe that at least one set provides different transcription. We take these 30 K words to be the critical cases. Our intuition is that if two G2P systems: one trained on a smaller version of the basic lexicon, and another trained on a larger version of the basic lexicon provide the same transcription for a word, then the word is a trivial case for phonetic transcription. We then manually verify the phonetic transcriptions of these 30 K words taking help from 3 linguists and following Chowdhury (2016), and consider these 30 K words as critical cases for phonetic transcription. We categorize the critical cases into 7 categories and observe the distribution of the critical transcriptions into these 7 categories. These 7 cate-Algorithm 1 Algorithm for Compressing a Dictionary or Lexicon 1: sd sorted sourceDictionary 2: dd sorted destinationDictionary 3: a:grs grapheme sequence of lexicon 4: entry a 5: add sd [0] to dd 6: i = 1 7: while i  = length ( sd ) do 8: pw = sd [ i (cid:0) 1] 9: cw = sd [ i ] 10: if length ( pw:grs ) (cid:21) 3 & pw:grs is prefix of cw:grs then 11: continue 12: else 13: add cw to dd 14: i i + 1 gories capture most of the errors. The categories are:  Open Close Vowel Confusion: G2P system provides pronunciation as close vowel that should be pronounced as open vowel ideally, and vice-versa. For example, correct phoneme of (cid:300) (frog) is /b E n g/, but if G2P system provides output /b e n g/, then it is an error under this category as in the place of open vowel (here, /E/), G2P system is giving close vowel (here, /e/). Inherent Vowel Confusion: G2P system does not provide inherent vowel as output where there should be an inherent vowel ideally. For example, correct phoneme of  (morning) is /sh O k a l/, but if G2P system provides output /sh k a l/, then it is an error under this category as the output of G2P does not give inherent vowel (here, /O/). Diphthong Confusion: G2P system does not provide falling diphthong in output where there should be a falling diphthong ideally. Or, system does not provide rising diphthong in output where there should be a rising diphthong ideally. For example, correct phoneme of  (friend) is /sh o iw/, but if G2P system provides output /sh o i/, then it is an error under this category as the output of G2P does not capture the falling diphthong (here, /o iw/). be /sh/, and vice-versa. For example, correct phoneme sequence of  (organization) is /sh O N g O Th o n/, but if G2P system provides output /s O N g O Th o n/, then it is an error under this category as the output of G2P gives /s/ in place of /sh/. s or ch Confusion: G2P system provides /s/ in phonetic transcription, where there should be /ch/, and vice-versa. For example, correct phoneme sequence of  (umbrella) is /ch a t a/, but if G2P system provides output /s a t a/, then it is an error under this category as the output of G2P gives /s/ in place of /ch/. Nasal Confusion: G2P system does not provide any nasal vowel where there should be a nasal vowel, and vice-versa. For example, correct phoneme sequence of  (moon) is /c an d/, but if G2P system provides output /c a d/, then it is an error under this category as the output of G2P gives /a/ in place of /an/. Other Vowel Confusion: G2P system provides completely different vowel than the corresponding vowel that should ideally be in that position of the phoneme sequence. Note that, in the other error categories, for each position in the phoneme sequence, the generated and ideal phonemes were somehow related. But in this category, at a specific position of the phoneme sequence, the generated and ideal phonemes are completely different. For example, correct phoneme sequence of (cid:300) (perseverance) is /o d dh o b O sh a ew/, but if G2P system provides output /o d dh a b O sh a ew/, then it is an error under this category as the output of G2P gives /a/ in place of /o/ (fourth phoneme). Algorithm 2 compares a machine-generated lexicon with a reference lexicon (manually verified), where both the lexicons have same grapheme sequences, but the corresponding phoneme sequences may be different. This algorithm counts how many errors of each category are there in the machine generated lexicon. The algorithm takes each entry of the generated lexicon and increases the count of the corresponding error category (if an error is present there). We train attention mechanism based Transformer model on each of the 4 lexicons and get Algorithm 2 Comparing a Generated Lexicon ( gl ) with Reference Lexicon ( rl ) 1: N total number of entries in each lexicon 2: A; B; C; D; E; F; G are Open Close Vowel, s or sh, s or ch, Nasal, Dipthong, Other Vowel, and Inherent confusions, respectively, all initially zero 3: H denotes other errors not captured by the 7 categories, initially zero 4: vl and wl are lists of vowels and weak vowels respectively 5: a:phs phoneme sequence of lexicon 6: entry a 7: i 0 8: while i  = N do 9: g = gl [ i ] :phs 10: r = rl [ i ] :phs 11: M = min ( length ( g ) ; length ( r )) 12: j 0 13: while j  = M do 14: x = g [ j ] 15: y = r [ j ] 16: if x = y then 17: continue 18: ( x; y ) sorted ( x; y ) 19: T otal _ error = T otal _ error + 1 20: if ocConfusion ( x; y ) then 21: A A + 1 22: else if ( x; y ) = (  s  ;  sh  ) then 23: B B + 1 24: else if ( x; y ) = (  ch  ;  s  ) then 25: C C + 1 26: else if x +  n  = y then 27: D D + 1 28: else if x in wl or y in wl then 29: E E + 1 30: else if x in vl and y in vl then 31: F F + 1 32: else if removeV owel ( g ) = removeV owel ( r ) then 33: G G + 1 34: else 35: H H + 1 36: j j + 1 37: i i + 1 4 G2P models. We find the phoneme representation of 30 K critical cases using each of the 4 G2P models. Using Algorithm 2, we count the errors of each category for each of the 4 models. We report Algorithm 3 Procedure: ocConfusion ( x; y ) (Checks if open close vowel confusion) 1: ocSet [(  O  ;  o  ) ; (  E  ;  e  ) ; 2: (  On  ;  on  ) ; (  En  ;  en  )] 3: if ( x; y ) in ocSet then 4: return True 5: else 6: return False Algorithm 4 Procedure: removeVowel (phoneme_sequence) 1: return phoneme_sequence removing all vowels from it the results in Table 2 and Figure 1. Here, other error denotes the errors that are not captured by these 7 categories. We see from these results that most of the errors are under Open Close vowel, s or sh, Diphthong, and Inherent Vowel confusions. 5 Developing an Improved G2P System for Bangla Language We develop an improved lexicon and use two machine learning based models trained on our lexicon to develop an improved G2P system for Bangla language. 5.1 Developing an Improved Lexicon For developing an improved lexicon, we include with Google's lexicon the manually verified 30 K non-trivial or critical entries. Also, we include the 70 K entries in which all of the 4 models (trained on each of the 4 versions of Google lexicon) unanimously agreed. After removing the repeated entries with same grapheme sequence, our lexicon consists of around 90 K entries. In case of repeated entries having same grapheme sequence, we carefully keep only the entry that has been manually verified. 5.2 G2P Models We use Neural Sequence to Sequence models. In these models, a conditional distribution of a sequence (here, phoneme sequence) is learned conditioned on another sequence (here, grapheme se-quence). We train two following Sequence to Sequence models on our lexicon for G2P conversion: LSTM-RNN : This is a plain Sequence to Sequence model that incorporates an encoder and decoder mechanism. Recurrent Neural Network Figure 1: Categorization of errors in critical cases, here each of the 60 K; 40 K; 24 K; and 12 K denotes the model trained on that particular lexicon. (RNN) is usually utilized in encoder and decoder design. For addressing vanishing gradient problem in RNN, Long-Short Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) is used. We follow Yao and Zweig (2015) for implementation. Transformer Model : Transformer Model uses attention mechanism. Attention mechanism provides improvement upon plain Sequence to Sequence by easing the flow of information from source sequence to destination sequence. We follow Vaswani et al. (2017) for implementation. We show the performance of both of these models in Section 6. We observe that Transformer Model provides higher token-level accuracy (lower Word Error Rate) than LSTM-RNN. 6 Experimental Results We run extensive simulations and use two measures for evaluating the performances of the systems: Word Error Rate (WER): For calculating Word Error Rate (WER), we use the following formula: WER = ET where E denotes the number of words that have disagreement in their generated phoneme sequence and reference phoneme sequence, and T denotes the total number of words. Phoneme Error Rate (PER): For calculating Phoneme Error Rate (PER), we use the following formula: PER = I + S + D T where I , S , D denote respectively the total number of insertion, substitution, and deletion operations needed for all the words to align the generated phoneme sequence with the reference phoneme sequence for each word. T denotes the total number of phonemes present in all the words. Our best performing model is Transformer Model. We use batch size of 4096. Our neural network has 3 hidden layers, each containing 256 nodes. We use a computer having 8GB RAM, Intel Core i7 CPU, and Nvidia Geforce 1050 GPU for running all of the simulations. For each model, we run the simulations for around 110 K iterations taking around 5 hours. 6.1 Performance on Critical Cases We report the experiment results of Google's lexicon on critical cases in Table 3. We do not report our lexicon here as critical cases are already included in our lexicon. 6.2 Performance Comparison In General For comparing the performances of models trained on our lexicon and Google's lexicon, we randomly Error Type 60K 40K 24K 12K total error 6415 7222 8087 10400 Open Close Confusion (%) 32.0 30.7 34.8 23.6 Inherent Vowel Confusion (%) 28.4 36.7 32.0 40.1 s or sh confusion (%) 14.6 15.3 14.2 12.8 Dipthong confusion (%) 11.6 9.8 7.6 9.4 Other Vowel Confusion (%) 2.1 1.3 4.2 3.1 s or ch confusion (%) 0.8 0.7 0.2 0.5 Nasal Confusion (%) 0.2 0.1 0 0 Other Error (%) 10.4 5.4 7.1 10.6 Table 2: Error classification of 30 K critical cases, here each of the four rightmost columns denotes the model trained on that particular lexicon. Lexicon Model WER (%) PER (%) Google LSTM-RNN 25.7 3.26 Transformer Model 23.6 2.71 Table 3: Performance on Critical Cases take 9000 entries from our manually verified 30 K critical cases as test set. We use this test set for evaluating all the models. Though our actual lexicon contains these 9000 entries, we do not keep them in our lexicon while doing the experiments to fairly evaluate the performances of the lexicons. For both lexicons, we keep 90% of the lexicon in train set and remaining 10% in validation set. Table 4 shows the result. Models trained on our lexicon outperforms those trained on Google's lexicon by a significant margin. Moreover, Transformer Model performs better than LSTM-RNN. Figure 2 and Table 5 categorize the errors of systems trained on 3 types of lexicons (Roman-ized version of our lexicon is discussed in section 6.3) by using Algorithm 2. Here, we report the results of Transformer Model only as it has been better performing that LSTM-RNN in our experiments. We observe most of the errors are related to Open Close vowel, s or sh, Diphthong, and Inherent Vowel confusions this finding also conforms to Figure 1 and Table 2, which were error catego-Lexicon Model WER (%) PER (%) Google LSTM-RNN 17.1 2.32 Transformer Model 14.8 1.88 Our Lexicon LSTM-RNN 10.5 1.42 Transformer Model 9.8 1.33 Table 4: Performance Comparison In General Error Type Our Lexicon Romanized Lexicon Google Lexicon Total Error 1337 1406 2961 Inherent Vowel Confusion (%) 35.6 34.5 33.8 Open Close Confusion (%) 30.1 30.4 27.9 s or sh confusion (%) 13.3 13.0 12.1 Diphthong confusion (%) 10.7 9.8 13.1 Other Vowel Confusion (%) 1.9 4.3 2.7 s or ch confusion (%) 0.3 0.2 0.2 Nasal Confusion (%) 0.2 0.43 0.6 Other Error (%) 7.9 7.4 9.7 Table 5: Performance comparison on different error categories. Here each of the three rightmost columns denotes the model trained on that particular lexicon. rization of critical cases. 6.3 Effect of Romanization For doing experiments on the effect of romanization on G2P conversion, we romanized all grapheme sequences in our lexicon to prepare romanized counterpart of our lexicon. During romanization, each grapheme symbol in Bangla is replaced with a single English letter except that if a consonant grapheme is not followed by a vowel grapheme, O was added after the romanized symbol of that consonant as the roman symbol for ``'' , which is usually inherently pronounced in such cases. All the symbols used for romanization were completely disjoint to avoid any ambiguity in the lexicon. Figure 3 shows the WER and PER Figure 2: Performance Comparison on Different Error Categories Figure 3: Comparison in terms of WER and PER of systems trained on 3 types of lexicons. Here, we report the results of Transformer Model only as it has been better performing than LSTM-RNN in our experiments. Both versions of our lexicon perform better (lower WER and lower PER) than Google's lexicon. Also romanization does not significantly increase or decrease the performance. Figures 4 and 5 show respectively the Word Recognition Accuracy ( 1 (cid:0) WER) and Phoneme Recognition Accuracy ( 1 (cid:0) PER) with respect to number of iterations run during simulation. Both versions of our lexicon perform better (higher Word Recognition Accuracy and higher Phoneme Recognition Accuracy) than Google's lexicon. Figure 6 shows Negative Log Perplexity vs number of iterations. Both versions of our lexicon provide higher negative log perplexity than Google's lexicon. Figure 4: Word Recognition Accuracy vs Iteration 6.4 Effectiveness of Our Identified Critical Cases In this section, we want to establish that the improved performance of our lexicon comes not only from the increase in number of training samples, but also due to the fact that the critical cases identified by our novel methodology have been added as training samples. For this, we prepare a new training lexicon by combining a portion of the Google lexicon with a portion of our identified critical cases. As we have kept 9 K entries from the critical cases as our test set, we take the remaining 21 K critical cases and combine them with the randomly taken 39 K entries from Google lexicon to prepare a new lexicon of size 60 K . While taking entries from Google lexicon, we ensure that we do not take any repeated entry that has already been in the critical cases and added to the new lexicon. Figure 5: Phoneme Recognition Accuracy vs Iteration Figure 6: Negative Log Perplexity vs Iteration Lexicon Model WER (%) PER (%) Google LSTM-RNN 17.1 2.32 Transformer Model 14.8 1.88 New Lexicon LSTM-RNN 12.6 1.54 Transformer Model 11.2 1.49 Table 6: Effectiveness of critical cases. Both lexicons are of size 60 K . New Lexicon consists of 21 K critical cases and 39 K entries from Google lexicon. We then compare the performance of this new lexicon with the Google lexicon, both of which are of same size ( 60 K ), on our test set. The results are in Table 6. The results clearly show that even in the case of same sized lexicons, our identified critical cases can significantly improve the performance as evidenced by the lower WER and lower PER than those for the Google lexicon. 7 Conclusion and Future Works In this paper, we have identified the critical cases, categorized the errors, and increased the current state-of-the-art accuracy in G2P conversion for Bangla language by developing an improved lexicon. In future, we will do classification of errors using unsupervised clustering approaches. Tables 2 and 5 both show that most of the errors occur in Open Close Vowel, s or sh, Diphthong, and Inherent Vowel confusion categories. In future, we will focus on each category to devise novel methodology for mitigating the errors of that category and in turn further increase the accuracy of G2P system in Bangla. 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