Character-Level Bangla Text-to-IPA Transcription Using Transformer
  Architecture with Sequence Alignment
Jakir Hasan Shrestha Datta Ameya Debnath
The International Phonetic Alphabet (IPA) is indispensable in language
learning and understanding, aiding users in accurate pronunciation and
comprehension. Additionally, it plays a pivotal role in speech therapy,
linguistic research, accurate transliteration, and the development of
text-to-speech systems, making it an essential tool across diverse fields.
Bangla being 7th as one of the widely used languages, gives rise to the need
for IPA in its domain. Its IPA mapping is too diverse to be captured manually
giving the need for Artificial Intelligence and Machine Learning in this field.
In this study, we have utilized a transformer-based sequence-to-sequence model
at the letter and symbol level to get the IPA of each Bangla word as the
variation of IPA in association of different words is almost null. Our
transformer model only consisted of 8.5 million parameters with only a single
decoder and encoder layer. Additionally, to handle the punctuation marks and
the occurrence of foreign languages in the text, we have utilized manual
mapping as the model won't be able to learn to separate them from Bangla words
while decreasing our required computational resources. Finally, maintaining the
relative position of the sentence component IPAs and generation of the combined
IPA has led us to achieve the top position with a word error rate of 0.10582 in
the public ranking of DataVerse Challenge - ITVerse 2023
(https://www.kaggle.com/competitions/dataverse_2023/).