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Training automatic speech recognition (ASR) systems requires large amounts ofdata in the target language in order to achieve good performance. Whereas largetraining corpora are readily available for languages like English, there existsa long tail of languages which do suffer from a lack of resources. One methodto handle data sparsity is to use data from additional source languages andbuild a multilingual system. Recently, ASR systems based on recurrent neuralnetworks (RNNs) trained with connectionist temporal classification (CTC) havegained substantial research interest. In this work, we extended our previousapproach towards training CTC-based systems multilingually. Our systems featurea global phone set, based on the joint phone sets of each source language. Weevaluated the use of different language combinations as well as the addition ofLanguage Feature Vectors (LFVs). As contrastive experiment, we built systemsbased on graphemes as well. Systems having a multilingual phone set are knownto suffer in performance compared to their monolingual counterparts. With ourproposed approach, we could reduce the gap between these mono- and multilingualsetups, using either graphemes or phonemes.

Phonemic and Graphemic Multilingual CTC Based Speech Recognition