While research in large vocabulary continuous speech recognition (LVCSR) has sparked the development of many state of the art research ideas, research in this domain suffers from two main drawbacks. First, because of the large number of parameters and poorly labeled transcriptions, gaining insight into further improvements based on error analysis is very difficult. Second, LVCSR systems often take a significantly longer time to train and test new research ideas compared to small vocabulary tasks. A small vocabulary task like TIMIT provides a phonetically rich and hand-labeled corpus and offers a good test bed to study algorithmic improvements. However, oftentimes research ideas explored for small vocabulary tasks do not always provide gains on LVCSR systems. In this paper, we address these issues by taking the standard Ȝrecipeȝ used in typical LVCSR systems and applying it to the TIMIT phonetic recognition corpus, which provides a standard benchmark to compare methods. We find that at the speaker-independent (SI) level, our results offer comparable performance to other SI HMM systems. By taking advantage of speaker adaptation and discriminative training techniques commonly used in LVCSR systems, we achieve an error rate of 20%, the best results reported on the TIMIT task to date, moving us closer to the human reported phonetic recognition error rate of 15%. We propose the use of this system as the baseline for future research and believe that it will serve as a good framework to explore ideas that will carry over to LVCSR systems.
['Tara N. Sainath', 'Bhuvana Ramabhadran', 'Michael Picheny']
An exploration of large vocabulary tools for small vocabulary phonetic recognition