Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer (2023)

Abstract: In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.

## introduction this paper describes the submission by our team to the 2022 version of the sigmorphon graphemeto-phoneme conversion challenge @xcite . here we describe our efforts to improve grapheme-to-phoneme mapping for low-resource languages in a non-neural context using only data augmentation techniques. the problem in the low-resource condition was to map from graphemes to phonetic segments with extremely limited data. specifically, there were 10 languages with 100 training pairs and 100 development pairs. each pair was a word in its orthographic representation and a phonetic transcription of that word. in addition, for each language, there were up to 1000 additional training pairs in a "related" language. systems were then tested on 100 additional pairs for each language. the 10 languages are given in ## initial neural approaches we started with a fairly generic transformer system inspired by one of the 2020 baseline systems @xcite . the system we used is adapted from the opennmt base @xcite and is similar to the one used by @xcite in the 2021 challenge. there is a 512-element embedding layer in both encoder and decoder. there are six layers in both encoder and decoder and each layer also has 512 nodes. the systems are connected by a 8-head attention mechanism @xcite . training proceeds in 1,000 steps and the decay method is noam. optimization is adam, the batch size is 8, and the dropout rate is 0.1. 1 using this system and running 1000 steps, performance on validation data is terrible as seen in to get a sense of how much data might be required to get decent performance, we ran a similar transformer configuration over subsets of the cmu pronouncing dictionary (weide, 1998) for 5 epochs and got the performance in ## an hmm-based approach based on how poorly our neural approaches performed with such limited data, we went back to classical hmm-based approaches, specifically selecting the phonetisaurus system @xcite . in ## augmentation steps we tried several kinds of augmentations. the first was the substring approach developed by @xcite . in this approach plausible alignments from the beginnings and ends of words are recombined. in the original approach, techniques were used to increase the likelihood that the alignment point occurred at a plausible c-v or v-c juncture. we found that this did not work for all languages in our test set, presumably due to how limited the data were. we therefore disabled this feature. the other augmentation we used applied to the transfer data. if one looks at the training pairs, it's apparent that in a number of cases, the languages are not terribly similar. for example, irish and welsh are indeed related and the diligent linguist can easily find cognates. for example, the welsh word for 'book' is llyfr [ì1v1r] and the irish word is leabhar [l j @ur g ]. the welsh word for 'man' is dyn [d1:n]; the irish word for 'person' is duine [d g in j @]. there are also similar grammatical features. for example, both languages use initial consonant mutation as a grammatical mechanism, both have vso word order, and both have inflected prepositions. on the other hand, the orthographic conventions of the two languages are extremely divergent, as are the phonetic inventories. for example, irish has a contrast between palatalized and plain consonants that is completely absent in welsh. this contrast is reflected in the orthography where adjacent front vowel letters i and e indicate that a consonant is palatalized. this orthographic practice applies on both sides of a consonant. thus, if a consonant is intervocalic and palatalized, it will have front vowels on both sides; if it's not palatalized, it will have back vowels. on the other hand, unlike irish, welsh strikingly can use w and y as vowels giving rise to words that seem quite unpronouncable, e.g. with this in mind, we tried approaches that would limit the transfer data to just those pairs that were most like the target language. we tried three approaches in this vein. first, we only took pairs where the phonetic segments of the transfer language were in the inventory of the target language. second, we further restricted the pairs to only those where the phonetic bigrams of the transfer language all occurred in the target language. finally, we only included pairs where all orthographic characters in the transfer language occurred in the target language. different combinations of these options appear in looking at performance on the test data of course varies slightly from what we saw with the validation data so we give those results in ## conclusion in conclusion, we've seen several effects. first, a simple encoder-decoder or transformer does not perform well with so few data. second, an hmmbased approach does better, and does better still when we restrict the kind of transfer data that is used. specifically, transfer data should be restricted based on how similar it is to the target language. similarity in terms of phonetics is clearly beneficial, but similarity in terms of orthography seems to help as well. finally, we saw that the substring recombination technique of ryan and hulden (2020) can be added on top of these moves for an additional benefit.