# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
## 3 Multilingual G2P Model  
Designing a G2P system poses a significant challenge as it involves creating a many-to-many mapping system that not only learns the correspondence between a single grapheme and a phoneme, but also handles cases where a phoneme is represented by multiple graphemes (e.g.,'sh' \(\rightarrow\) 'S'). Such mapping can exhibit inconsistencies and ambiguity, particularly in languages such as English, where names and foreign words introduce additional complexities. There is also the problem of co-articulation between different languages. \"Co-articulation\" is a term within the field of phonetics, referring to the phenomenon in which distinct speech units, such as phonemes or syllables, mutually influence one another during the process of articulation. This mutual influence results in temporal and spatial adjustments, facilitating the production of seamless speech. In other words, one segment of speech is influenced by the preceding and succeeding segments, leading to a certain degree of overlap and modification. For instance, when we articulate a word or a sentence, the phonemes within it are not strictly isolated, but rather interact with one another. This interaction is due to the necessity of our articulatory organs to prepare for movement to the next articulatory position. During this process, the pronunciation of a preceding phoneme may impact the articulation of a subsequent phoneme. This phenomenon of co-articulation enhances the efficiency and coherence of speech production. For example, in the English word \"hand\", the articulation of the /n/ sound is influenced by the preceding /ae/ sound, resulting in the /n/ sound being produced with the tongue positioned close to the alveolar ridge. In the Tibetan word \"(language), the initial \"s\" affects the articulation of the following consonant \"(\"), showing co-articulation.  
To tackle these challenges, sequence-to-sequence (Seq2 Seq) neural network models have been employed to acquire the mappings between graphemes and phonemes, allowing for variable lengths in both input and output sequences. Originally devised for machine translation, Seq2 Seq models have found extensive applications across various domains, including generative language models. Recurrent Seq2 Seq models offer a distinct advantage by considering the input history to determine the output state. Consequently, they frequently outperform n-gram models in classification tasks. This superiority stems from the heavy reliance of n-gram models on the preceding n graphemes. When confronted with sequence-related challenges demanding long-term contextual information and embedding representations of input sequences, recurrent neural networks (RNNs) emerge as a more  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Word** & **Graphene** & **Universal Phonemes** \\ \hline Chinese & [] & G U O & gg uu oo \\ \hline English & go & go & gg oo \\ \hline Tibetan & \# & \# & mm \\ \hline Korean & \#[] & \# & \# & hk eK chK ee \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data samples.  
appropriate selection. Long-short-term memory (LSTM) networks exhibit enhanced suitability in managing extended sequences and can accommodate a greater number of layers due to their reduced susceptibility to vanishing and exploding gradients. Seq2 Seq models possess the capability to undergo training on multiple languages simultaneously, facilitating their application in multi-task and multi-modal learning scenarios. Within the realm of G2P conversion, these models enable the simultaneous acquisition of alignment and translation of graphemes and phonemes in an end-to-end manner. Consequently, they represent a natural choice for our multilingual G2P undertaking, particularly given their compatibility with large pronunciation lexicons characterized by relatively concise sequence lengths.