Can Continuous Speech Recognizers Handle Isolated Speech?

Continuous speech is far more natural and efficient than isolated speech for communication. However, for current state-of-the-art automatic speech recognition systems, isolated speech recognition (ISR) is far more accurate than continuous speech recognition (CSR). It is common practice in the speech research community to build CSR systems using only CS data. However, slowing of the speaking rate is a natural reaction for a user faced with the high error rates of current CSR systems. Ironically, CSR systems typically have a much higher word error rate when speakers slow down since the acoustic models are usually derived exclusively from continuous speech corpora. In this paper, we summarize our efforts to improve the robustness of our speaker-independent CSR system against speaking styles, without suffering a recognition accuracy penalty. In particular the multi-style trained system described in this paper attains a 7.0% word error rate for a test set consisting of both isolated and con..

Effect of reference set selection on speaker dependent speech recognition

Computer Science Departmen

Improved Hidden Markov Modeling for Speaker-Independent Continuous Speech Recognition

This paper reports recent efforts to further improve the perfor-mance of the Sphinx system for speaker-independent contin-uous speech recognition. The recognition error rate is signifi-cantly reduced with incorporation of additional dynamic fea-tures, semi-continuous hidden Markov models, and speaker clustering. For the June 1990 (RM2) evaluation test set, the error rates of our current system are 4.3 % and 19.9 % for word-pair grammar and no grammar respectively