# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
###### Abstract  
The construction of pronunciation dictionaries relies on high-quality and extensive training data in data-driven way. However, the manual annotation of corpus for this purpose is both costly and time consuming, especially for low-resource languages that lack sufficient data and resources. A multilingual pronunciation dictionary includes some common phonemes or phonetic units, which means that these phonemes or units have similarities in the pronunciation of different languages and can be used in the construction process of pronunciation dictionaries for low-resource languages. By using a multilingual pronunciation dictionary, knowledge can be shared among different languages, thus improving the quality and accuracy of pronunciation dictionaries for low-resource languages. In this paper, we propose using shared articulatory features among multiple languages to construct a universal phoneme set, which is then used to label words for multiple languages. To achieve this, we first developed a grapheme\(-\)phoneme (G2P) model based on an encoder\(-\)decoder deep neural network. We then adopted a near-optimal active learning method in the process of building the pronunciation dictionary to select informative samples from a large, unlabeled corpus and had them labeled by experts. Our experiments demonstrate that this method selected about 1/5 of the unlabeled data and achieved an even higher conversion accuracy than the results of the large data training method. By selectively labeling samples with a high uncertainty in the model, while avoiding labeling samples that were accurately predicted by the current model, our method greatly enhances the efficiency of pronunciation dictionary construction. multilingual; grapheme-to-phoneme conversion; pronunciation dictionaries; low-resource languages; active learning +
Footnote †: journal: Applied  
0000-0001-6000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-000000-000000-000000-  
State-of-the-art end-to-end multilingual automatic speech recognition (ASR) systems combine the acoustic model, pronunciation dictionary, and language model into a single neural network, resulting in models that are even more data-hungry and unsuitable for low-resource multilingual speech recognition. Furthermore, the efficacy of end-to-end automatic speech recognition (ASR) models utilizing character or word-based modeling units is limited in terms of effectively accommodating out-of-vocabulary (OOV), unless pronunciation dictionaries are employed. As a result, an increasing number of scholars are exploring the development of multilingual pronunciation dictionaries [1; 2; 3; 4; 5]. A multilingual pronunciation dictionary is constructed to solve the pronunciation problem in multilingual speech technology. Different languages have different phonetic systems and phonetic features, which require separate construction of pronunciation dictionaries. However, for low-resource languages, the construction of pronunciation dictionaries becomes difficult due to the lack of large-scale corpus and phonetic resources. Hence, the establishment of multilingual pronunciation dictionaries enables the conversion of pronunciation data from various languages into the pronunciation data of low-resource languages, facilitating knowledge sharing across different languages. Consequently, this approach contributes to the development of pronunciation dictionaries for low-resource languages. For instance, when a low-resource language shares certain phonemes with a high-resource language, an existing pronunciation dictionary for the latter can assist in constructing a pronunciation dictionary for the former. Primarily, this methodology has been proven to be advantageous for low-resource languages constrained by limited data [5; 6]. Regarding categorization, existing studies on the construction of multilingual pronunciation dictionaries can be classified into rule-based methods and deep-learning-based methods.