A Two-Step Approach for Data-Efficient F rench Pronunciation Learning (2024)

Abstract: Recent studies have addressed intricate phonological phenomena in French, relying on either extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. However, creating such resources is expensive and non-trivial. To this end, we propose a novel two-step approach that encompasses two pronunciation tasks: grapheme-to-phoneme and post-lexical processing. We then investigate the efficacy of the proposed approach with a notably limited amount of sentence-level pronunciation data. Our findings demonstrate that the proposed two-step approach effectively mitigates the lack of extensive labeled data, and serves as a feasible solution for addressing French phonological phenomena even under resource-constrained environments.

## introduction phonetic information plays a crucial role in text-tospeech systems, improving the clarity and naturalness of synthetic speech. grapheme-to-phoneme (g2p) relationships are typically modeled using a sizeable set of phonetic transcriptions to predict the pronunciation of out-of-vocabulary words. however, pronunciation learning in french remains challenging due to its intricate phonetic structure and phonological phenomena such as linking (enchaînement) and liaison. these phenomena mediate between words by modifying phonemes and their placement @xcite . linking is the articulation of a consonant-final word and its re-syllabification with the following vowel-initial word @xcite . for example, when a consonant-final word une -[yn] precedes a vowel-initial word amie -[a.mi], the phoneme "n" is resyllabified and positioned adjacent to the primary phoneme of the following word, as illustrated in the example below: une [yn] amie [a.mi] → une amie [y.na.mi] liaison refers to the pronunciation of a silent consonant-final word to its phoneme or another when it is followed by a vowel-initial word @xcite . for instance, when the determiner mes is an initial word, its pronunciation is altered depending on the following word. if the following word, such as frères, begins with a consonant, the "s" grapheme remains silent in the corresponding pronunciation. mes [me] frères [fkek] → mes frères [me.fkek] however, when it is followed by a vowel-initial word like amis, the "s" is pronounced as @xcite . mes [me] amis [ami] → mes amis [me.za.mi] these phonetic modifications are influenced by various factors and contexts, leading to numerous exceptions. such variability further adds to the complexity of addressing phonological phenomena 1 . recent studies have focused on modeling these phonological phenomena through two main approaches: post-lexical rules (plr) @xcite and data-driven methods @xcite @xcite . while both plr and data-driven methods achieve decent performance, they demand deeper phonological/linguistic knowledge and a substantial amount of human-annotated sentence-level data, respectively. unfortunately, existing approaches without such expensive resource commitments have not been satisfactory. in this paper, we propose a novel two-step approach to address the challenge of learning french pronunciation with limited resources. specifically, we explicitly decompose the intricate and comprehensive pronunciation task into two sub-tasks: g2p conversion and post-lexical processing. first, we leverage a large amount of easily accessible word-level pronunciation data to train the autoregressive transformer (art)-based g2p model (comini et al., 2023; yolchuyeva et al., 2019; yu et al., 2020 @xcite and generate correct pronunciations for corresponding words. second, we adopt a shallow non-autoregressive transformer (nart) @xcite as the post-lexical phonetization model to process phonological phenomena between the pronunciations of individual words. this model is trained on our manually constructed dataset, comprising a modest number of sentence-level examples. accordingly, we assess whether the proposed approach can effectively leverage a limited set of sentence-level examples to overcome the challenges, and further analyze how varying the size of these resources impacts performance. the experimental results reveal that the proposed approach successfully addresses intricate phonological phenomena, utilizing only around 2k examples, with even 1.5k examples proving somewhat effective. ## related work plr is one of the essential modules used to address phonological phenomena in the french textto-speech front-end, yet it requires extensive phonological and linguistic knowledge to construct a comprehensive set of hand-crafted post-lexical rules. although only a few studies foot_1 @xcite provide initial guidelines for manually constructing post-lexical rules, the implementation of their intricate interactions still necessitates the deep knowledge and substantial efforts of linguistic experts. to alleviate this burden, data-driven approaches have also been proposed @xcite @xcite . unlike plr, these approaches demonstrate performance improvements by leveraging large-scale sentencelevel pronunciation datasets, even without linguistic knowledge. @xcite reported using 3 proposed method in this section, we describe our two-step approach to french pronunciation learning. our approach addresses the extensive and complex pronunciation task by explicitly decomposing it into two key subtasks: g2p conversion and post-lexical processing. the overall architecture is illustrated in figure 1 . ## conclusion in this paper, we present an effective two-step approach for french pronunciation learning. our approach alleviates the burden of extensive resources by decomposing the intricate and comprehensive pronunciation task into two sub-tasks, thereby facilitating greater leverage of a modest number of sentence-level examples. the empirical analysis demonstrates the efficacy of our proposed approach in addressing phonological phenomena even in resource-constrained environments.