The Influence of Regional Pronunciation Variation on Children’s Spelling and the Potential Benefits of Accent Adapted Spellcheckers (2021)

Abstract: A child who is unfamiliar with the correct spelling of a word often employs a “sound it out” approach: breaking the word down into its constituent sounds and then choosing letters to represent the identified sounds. This often results in a misspelling that is orthographically very different to the intended target. Recently, efforts have been made to develop phonetic based spellcheckers to tackle the more deviant nature of children’s misspellings. However, little work has been done to investigate the potential of spelling correction tools that incorporate regional pronunciation variation. If a child must first identify the sounds that make up a word, it stands to reason their pronunciation would influence this process. We investigate this hypothesis along with the feasibility and potential benefits of adapting spelling correction tools to more specific language variants - particularly Irish Accented English. We use misspelling data from schoolchildren across Ireland to adapt an existing English phonetic-based spellchecker and demonstrate improvements in performance. These results not only prompt consideration of language varieties in the development of spellcheckers but also contribute to existing literature on the role of regional accent in the acquisition of writing proficiency.

## introduction children's spelling has long been considered to be rooted in phonetics; being influenced by pronunciation and speech sound similarity @xcite . misspellings are often 'creative' in that they reflect the phonetic judgements of the child and stem from their efforts to use letters to represent the sounds of their spoken language. as such, these misspellings are often unique and can deviate heavily from the correct spelling making the automatic correction of such errors a non-trivial task. nowadays, a popular focus in early literacy education is the teaching of phonics with schools across the english speaking world incorporating the method into their curricula @xcite . phonics based approaches to reading and writing involve teaching the relationship between letters and sounds. for example, the word 'cat' would be broken down into the letters 'c', 'a', 't' and the corresponding phonemes /k/, /ae/, /t/ foot_0 . when tackling the spelling of an unfamiliar word, children are then encouraged to adopt a "sound it out" approach by identifying the phonetic sequence of the word and the letters which represent these sounds. this approach is heavily relied upon by low achieving spellers @xcite . despite the phonetic nature of children's spelling and the employment of a literacy education methodology that is intrinsically linked to spoken words, little work has been carried out towards developing spellcheckers that account for systematic pronunciation variation. much of the existing research on the interaction between spoken variation and literacy acquisition typically focuses on children with an african american english (aae) dialect and the phonological and morphological deviation from what is referred to as mainstream american english (mae) (see section 2.2). in their systematic review, @xcite indicate that there is insufficient research on the relationship between regional accent or dialect and written english literacy in england. from the works reviewed, they surmise that there is no straightforward relationship between literacy achievement and language background and state that regional dialect has only a minor impact on writing. however, throughout the review, difficulties with spelling were not considered related to pronunciation variation and instead a result of the complexity between english orthography and pronunciation that affects all children regardless of their accent. whilst we agree that all children will face challenges in acquiring written literacy, we seek to explore whether regional variation might influence the sort of problems encountered by students and the types of misspellings they produce as a result. indeed, terry (2006) supposes that "while all children must learn to negotiate mismatches between speech and print in order to become good readers and writers, this process may be particularly problematic for children whose spoken language differs substantially from standard written forms". in much the same way as one might employ a targeted approach to teaching a foreign language based on the learner's native language, it could prove beneficial to children with regional language variants to be taught reading and writing skills with such variation in mind and to have access to tools designed specifically to handle their particular variant. this paper seeks to investigate the hypothesis that regional variation in pronunciation influences misspelling productions in children's writing. to do this, we explore whether an accent adapted spellchecker outperforms a baseline system which would suggest some underlying commonality between the misspellings produced by speakers of the same language variant. in doing so we also investigate the feasibility of developing such a system and the potential benefits it would have for speakers of a regional variant. an existing english phonetic based spelling correction tool is fine-tuned using misspelling data collected from schoolchildren across ireland. we demonstrate that this irish accented english (iae) adapted model achieves better results on our test set of iae misspellings. furthermore, we demonstrate that these gains in performance are not just a result of additional training, by fine-tuning the baseline model with non iae misspelling data which exhibits lesser performance gains. these results would suggest that the relationship between regional pronunciation variation and written literacy merits further exploration. probabilistic correction models, like that of @xcite or @xcite , exhibited improved performance on cognitive errors by modelling the likelihood of multiple edit operations. the correction of phonetic errors in particular has been tackled by incorporating pronunciation information as opposed to just orthographic representations. for example, by using weighted edit distances that consider the phonetic similarity between graphemes @xcite , by assigning an alphanumeric code intended to capture phonetic features @xcite , or by converting a misspelling to its corresponding phoneme sequence using letter-to-sound rules @xcite . in their recent survey, @xcite noted the emerging popularity of encoder-decoder architectures and deep neural networks that treat the spelling correction problem as one of statistical machine translation. spelling correction tools targeted specifically towards children have also been recently developed. @xcite released kidspell: a childoriented, rule-based, phonetic spellchecker. their system makes use of the phonetic rules of english to map letters to keys which aim to capture accurate phonetic representations. candidate suggestions for spelling correction are generated by identifying words with matching or similar phonetic keys. similarly, s-capade (o'neill et al., 2020), our own english spellchecker for children, produces candidate corrections that have the same or similar phoneme sequences to that predicted of the misspelled word. it generates a phoneme representation of the misspelling and calculates the weighted edit distance between the misspelling and a candidate correction based on the likelihood of phonemes being inserted, deleted, or substituted for others. the nature of s-capade's edit distance method allows for adaptation of the phoneme weights and we use this as a baseline for fine-tuning on iae speakers' data in this work. ## results the effectiveness of a spelling correction tool lies in its ability to suggest appropriate candidate corrections and in ranking the true target highly amongst them. as such, we determine the effectiveness of our models based on whether or not the target spelling occurred in the top k candidates (recall@k) and on the mean reciprocal rank (mrr). since there is only ever one relevant item in the list of candidates, namely the intended target word, recall@k is 1 if the target is in the top k candidates and 0 otherwise. mrr (see formula 3) is a measure of how highly the target typically ranks in the list of candidates with values closer to 1 indicating higher average target rankings. each iae-tuned distance matrix is supplied to the s-capade spelling correction tool (replacing the original base model matrix) which is then run on the misspellings in the corresponding test fold. to assess the performance of a spellchecker fine-tuned using iae data, we report the mean value across all test folds for each of our evaluation measures and compare with those obtained using the baseline s-capade model and on the holbrook fine-tuned model. the results can be seen in ## discussion from the results in in theory, the resultant distance matrix after iae fine-tuning should capture this commonality and the values should relate to aspects of the iae phonological system and phonetic realisations. figure 3 shows this tuned distance matrix, averaged across the ten folds, visualised as a heat map where a darker colour indicates a lower distance and thus higher degree of similarity between phonemes. accounting for the limited size of the iae corpus and the fact that not all features of iae will manifest in misspellings, the distance matrix does appear to exhibit the influence of iae phonology. many observed values could be considered general english features. for example, the /ng/ phoneme in a target being likely substituted for /n/ (as in the misspelling *bein (being)) or the high degree of variability among vowels. however, some values are specific to iae and dublin english in particular @xcite . for instance, the most likely phoneme to be substituted for /th/ in the target is /t/. this feasibly results from the fortition of dental fricatives to alveolar stops in iae prompting spellings such as *someting (something) and *tink (think). /ay/ onset raising is also common and could explain the misspelling *niss (nice) and the target /ay/ phoneme's most likely substitutes being the more central vowels /ah/, /ey/, and /ih/. the comparative performance of the iae-tuned spelling correction tool and the qualitative analysis of the resultant distance matrix would suggest that regional variation in pronunciation does somewhat influence misspelling productions in children's writing. the approach taken to fine-tune the existing model, albeit simple, has proven to be effective in adapting a correction tool to iae. this bodes well for future research and development in this area on a larger scale. the adaptation of literacy acquisition aids and educational tools to a particular language variant could offer more targeted support to children who may otherwise face obstacles in their learning. it is hoped that this work will prompt more extensive research into the interaction between spoken language and literacy and how this can be incorporated into the development of language tools in order to ensure all children can benefit regardless of language variant. ## limitations and future work this work is intended as a preliminary investigation into the idea that regional pronunciation variation manifests in the spelling errors of children and that an adapted spelling correction model is both feasible and beneficial to speakers of such variants. whilst initial results support the original hypothesis, it is important to note the limitations of the approach presented here. the iae-corpus used for tuning and testing is limited in size and scope. it is a relatively small sample of misspellings and is biased towards iae speakers from dublin. the collection of a larger dataset that is more representative of ireland as a whole is a planned area of future work pending experimental design and ethical approval. the weighted edit distance approach employed by s-capade only accounts for one-to-one phoneme mappings and is context independent. thus, potentially influential features of iae may not be adequately captured. for example, in dublin english, short vowels are typically lengthened when they occur before /r/ @xcite . this context dependent feature might be encoded in misspellings but the current model has no way of distinguishing the specific _/r/ environment. should this prove to be a significant shortcoming, a more complex method of spelling correction would need to be applied. a grapheme to phoneme tool trained on the cmu pronouncing dictionary is used to predict the phoneme sequences of misspellings. this does not always accurately represent the child's intended phoneme sequence. it could be possible to improve the predictions by training the tool on misspelling data. ## conclusion we have presented preliminary evidence of the influence of regional pronunciation variation on children's misspellings. by fine-tuning an english phonetic spellchecker to misspellings produced by iae speakers we demonstrate improved performance on similar test data. qualitative analysis of the re-sulting model suggests it was able to learn some phonological features of the language variant. future work will seek to further explore this relationship through the expansion of the iae misspelling dataset since its limited size and bias toward one particular county was identified as a limitation.