# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
###### Abstract
For rule-based approaches, each word and pronunciation are manually assigned by phonological experts. The pronunciation dictionaries are then developed based on the pronunciation rules of the word to train the neural network model. Nevertheless, the laborious nature of this approach arises from model size limitations, necessitating the manual acquisition and compilation of mapping data linking words to phonemes for pronunciation dictionaries. Consequently, the resulting pronunciation dictionaries tend to be small in scale and prone to inaccuracies. Conversely, certain scholars have explored statistical models leveraging conditional probability, such as n-gram techniques [7; 8]. This approach considers previous graphemes, making it fully contextualized and data-driven. However, the main drawback lies in the requirement for explicit alignment between graphemes and phonemes. Significant advancements have been made in the development of lexicon construction methods through deep learning. In the context of grapheme-to-phoneme conversion, [PERSON] and [PERSON] introduced the sequence-to-sequence model, employing recurrent neural network (RNN) and long-short term memory network (LSTM) techniques [9]. Currently, the relevant technology encounters several challenges. Rule-based methods offer high-quality pronunciation dictionaries, but demand extensive time and labor  
Figure 1: The location of pronunciation dictionaries in ASR. resources. Conversely, deep-learning-based methods are faster, but struggle to ensure accuracy. Moreover, low-resource languages suffer from limited training data, which makes direct retraining susceptible to overfitting. Constructing a pronunciation dictionary for such languages presents difficulties arising from inadequate data sources and expert labeling. To address these issues, recent research has focused on active-learning-based data selection, aiming to minimize the number of labeled instances required for building an effective classifier [10; 11; 12]. Therefore, this study aims to employ an active learning approach in pronunciation dictionary construction. While most existing active learning methods adopt a myopic strategy of labeling one unlabeled sample at a time, which is neither efficient nor optimal [13], non-myopic active learning is preferred. However, current methods tend to be greedy, selecting the top N unlabeled samples with the highest score [14]. Although this approach improves efficiency, it cannot guarantee learner performance. Although batch active learning reduces the number of iterations in the training process and enhances the efficiency of user labeling, the sample set remains locally optimal and fails to ensure the best classification accuracy using the minimum number of valuable samples [15; 16; 17; 18]. Active learning has been successfully applied in various natural language processing (NLP) tasks, including text classification, named entity recognition, and machine translation. Moreover, it has been shown to improve the quality of resulting models compared with learning on the full dataset [19; 20]. Traditional speech recognition and speech synthesis systems typically require the creation of distinct models and pronunciation dictionaries for each language, resulting in significant time and resource consumption. Conversely, a universal phoneme set facilitates the classification and standardization of phonemes across different languages, enabling more effective comparison and matching of pronunciation variations between languages. Moreover, speech recognition and synthesis often necessitate the utilization of numerous model parameters and computational complexity to represent diverse phonemes. A universal phoneme set assists in simplifying the algorithm and reducing computational complexity. Furthermore, certain low-resource languages face challenges in acquiring sufficient speech data for training speech recognition or synthesis models due to data scarcity [21].