End-to-end speech recognition systems have been successfully implemented and have become competitive replacements for hybrid systems. A common loss function to train end-to-end systems is connectionist temporal classification (CTC). This method maximizes the log likelihood between the feature sequence and the associated transcription sequence. However there are some weaknesses with CTC training. The main weakness is that the training criterion is different from the test criterion, since the training criterion is log likelihood, while the test criterion is word error rate. In this work, we introduce a new lattice based transcription loss function to address this deficiency of CTC training. Compared to the CTC function, our new method optimizes the model directly using the transcription loss. We evaluate this new algorithm in both a small speech recognition task, the Wall Street Journal (WSJ) dataset, a large vocabulary speech recognition task, the Switchboard dataset and a low resource speech recognition task, OpenKWS16. Results demonstrate that our algorithm outperforms a traditional CTC criterion, and achieves 7% WER relative reduction. In addition, we compare our new algorithm to some discriminative training algorithms, such as state-level minimum Bayes risk (SMBR) and minimum word error (MWE), with results supporting the benefits of the new algorithm.
Journal of Signal Processing Systems – Springer Journals
Published: Sep 30, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera