Title: Phonemic and Graphemic Multilingual CTC Based Speech Recognition,
Abstract: Training automatic speech recognition (ASR) systems requires large amounts of
data in the target language in order to achieve good performance. Whereas large
training corpora are readily available for languages like English, there exists
a long tail of languages which do suffer from a lack of resources. One method
to handle data sparsity is to use data from additional source languages and
build a multilingual system. Recently, ASR systems based on recurrent neural
networks (RNNs) trained with connectionist temporal classification (CTC) have
gained substantial research interest. In this work, we extended our previous
approach towards training CTC-based systems multilingually. Our systems feature
a global phone set, based on the joint phone sets of each source language. We
evaluated the use of different language combinations as well as the addition of
Language Feature Vectors (LFVs). As contrastive experiment, we built systems
based on graphemes as well. Systems having a multilingual phone set are known
to suffer in performance compared to their monolingual counterparts. With our
proposed approach, we could reduce the gap between these mono- and multilingual
setups, using either graphemes or phonemes.