DLM : A Decoupled Learning Model for Long-tailed Polyphone Disambiguation in M andarin (2024)

Abstract: Grapheme-to-phoneme conversion (G2P) is a critical component of the text-to-speech system (TTS), where polyphone disambiguation is the most crucial task. However, polyphone disambiguation datasets often suffer from the long-tail problem, and context learning for polyphonic characters commonly stems from a single dimension. In this paper, we propose a novel model DLM: a Decoupled Learning Model for long-tailed polyphone disambiguation in Mandarin. Firstly, DLM decouples representation and classification learnings. It can apply different data samplers for each stage to obtain an optimal training data distribution. This can mitigate the long-tail problem. Secondly, two improved attention mechanisms and a gradual conversion strategy are integrated into the DLM, which achieve transition learning of context from local to global. Finally, to evaluate the effectiveness of DLM, we construct a balanced polyphone disambiguation corpus via in-context learning. Experiments on the benchmark CPP dataset demonstrate that DLM achieves a boosted accuracy of 99.07%. Moreover, DLM improves the disambiguation performance of long-tailed polyphonic characters. For many long-tailed characters, DLM even achieves an accuracy of 100%.

## introduction grapheme-to-phoneme conversion (g2p) plays an important role in the chinese text-to-speech system (tts), aiming to convert chinese text to pinyin. one of the biggest challenges in g2p is polyphone disambiguation, whose goal is to select a correct pronunciation for the polyphonic character from a set of candidate pronunciations. there are a large number of polyphonic characters in chinese. so various approaches have been proposed to address polyphone disambiguation, primarily focusing on three problems: (i) how to solve * corresponding author the long-tailed distribution issue in datasets. (ii) how to effectively leverage the context of polyphonic characters. (iii) how to simply solve the polyphone disambiguation data sparsity problem. solutions for (i) are commonly realized by expanding the tail class corpus @xcite . although such methods improve the model's classification ability, the failure to learn from the original distribution of the dataset causes a decrease in representation ability. solutions for (ii) often involve extracting sequence features through neural networks @xcite or pre-trained models @xcite , supplemented by part-of-speech (pos) tags. however, most of these methods only concentrate on one dimension of context: global features from the entire sequence or local features from the neighboring characters. for (iii), the reliance on self-built datasets persists, but existing methods using selfbuilt datasets encounter challenges due to insufficient details or implementation difficulties. recently, decoupled learning in computer vision (cv) tasks divides the learning procedure into representation learning and classification learning @xcite , and demonstrates that the imbalanced distribution of long-tailed datasets is more conducive for learning high-quality representations and re-balanced data is helpful in classification. however, its experimental tests were only conducted on the balanced dataset. the method in @xcite proposed a unified bilateral-branch network (bbn), which does not decouple the learning process but applies a cumulative learning strategy to shift the learning from representation to classification. specifically, in classification learning, the reversed data is used to retrain the classifier, which improves the model's performance on tail classes. inspired by decoupled learning and bbn, this paper proposes a model called dlm (a decoupled learning model for long-tailed polyphone disam-biguation in mandarin), which can address the above three problems. dlm adopts decoupled learning on the long-tailed dataset to enhance both representation and classification ability. additionally, dlm integrates two improved attention mechanisms: convolution attention for local features extraction, and linear attention for global features extraction. between them, employing a gradual conversion strategy akin to human reading, dlm realizes the transition learning of context from local to global. finally, to evaluate the efficacy of decoupled learning in dlm, we leverage in-context learning (icl) related to large language models (llm) to generate a balanced polyphone disambiguation corpus easily and provide a detailed implementation. the contributions can be summarized as follows: ## method our proposed dlm is illustrated in figure 1(a ). the input sequence is initially processed by roberta to obtain the output vector h. subsequently, h passes through two branches. the first branch employs convolution attention, emphasizing the sequence local features. the second branch is a combination of linear attention and bilstm, concentrating on sequence global features. and the dropconnect is integrated into the second branch to prevent over-fitting. the outputs from these two branches are combined to derive the final output, where the weights for the two branches are obtained from α in the gradual conversion strategy. moreover, dlm adopts decoupled learning. the model is trained to learn representation in the first stage and the classifier is retrained in the second stage. ## conclusions in this paper, we propose a novel model dlm. in experiments, we validate the effectiveness of decoupled learning to solve the long-tail problem and prove that the original distribution of the longtailed dataset enhances the model's representation ability, while the re-balanced dataset contributes to classification ability. besides, we further confirm the efficacy of two improved attention mechanisms, particularly the convolution attention proposed in this paper. we find the significance of employing a gradual conversion strategy between the two improved attention to realize the transition learning of context from local to global when tackling the imbalanced dataset. finally, a simple approach for generating the polyphone disambiguation corpus via in-context learning is proposed and we build a balanced corpus to verify the validity of decoupled learning. overall, dlm demonstrates outstanding performance on the cpp dataset, especially achieving superior performance in handling long-tailed polyphonic characters.