Phoneme Level Language Models for Sequence Based Low Resource ASR
Table 1: Overview of the FLP Babel Corpora used in this work.
['[EMPTY]', 'Languages', '#Units', '#Train Utts', 'OOV%']
Building character-level n-gram language models is difficult due to the need for long contexts. However, with the use of RNNs and (Long Short Term Memory) However, this work was limited to producing only words and did not explore its capabilities at sentence level (the model cannot produce sentences). Though this work made interesting observations in deciding what languages to use for improving performance of multilingual models, they didn’t explore its capability to adaptation towards a new target language. There are multiple ways to decode CTC acoustic models. While the first one produces words from a fixed lexicon, it requires us to make word-level language models. On the contrary, the latter requires easier to train character language models but performs open-vocabulary decoding which may not always be optimal, especially in low resource scenarios. In this paper, we use 9 different languages from the IARPA BABEL Research Project (IARPA-BAA-11-02). We choose three languages, Cebuano, Mongolian and Amharic, as our unseen test languages and choose two other languages spoken in nearby regions from each of these three, i.e., Javanese, Tagalog, Turkish, Kazakh, Swahili, and Zulu, as our training languages. We hope to maximize closeness in language family and loan words by using this heuristic.