  This paper presents a comparison of a traditional hybrid speech recognition
system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free
end-to-end (TensorFlow implementation of multi-layer LSTM with CTC training)
models for German syllable recognition on the Verbmobil corpus. The results
show that explicitly modeling prior knowledge is still valuable in building
recognition systems. With a strong language model (LM) based on syllables, the
structured approach significantly outperforms the end-to-end model. The best
word error rate (WER) regarding syllables was achieved using kaldi with a
4-gram LM, modeling all syllables observed in the training set. It achieved
10.0% WER w.r.t. the syllables, compared to the end-to-end approach where the
best WER was 27.53%. The work presented here has implications for building
future recognition systems that operate independent of a large vocabulary, as
typically used in a tasks such as recognition of syllabic or agglutinative
languages, out-of-vocabulary techniques, keyword search indexing and medical
speech processing.
