A Comparison of Hybrid and End-to-End Models for Syllable Recognition
- 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 searchThis 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.…


| Author: | Korbinian Riedhammer, Sebastian P. BayerlORCiD |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-030-27947-9_30 |
| ArXiv Id: | http://arxiv.org/abs/arXiv:1909.12232 |
| Document Type: | conference proceeding (article) |
| Language: | English |
| Date of first Publication: | 2019/09/19 |
| Reviewed: | Begutachtet/Reviewed |
| Release Date: | 2024/07/05 |
| Tag: | speech recognition, language model, CTC, end-2-end, syllables |
| Pagenumber: | 8 |
| Konferenzangabe: | International Conference on Text, Speech and Dialogue (TSD) |
| institutes: | Fakultät Informatik |
| Licence (German): |
