Syllable-Based Acoustic Modeling With Lattice-Free MMI for Mandarin Speech Recognition

Abstract

Most automatic speech recognition (ASR) systems in past decades have used context-dependent (CD) phones as the fundamental acoustic units. However, these phone-based approaches lack an easy and efficient way for modeling long-term temporal dependencies. Compared with phone units, syllables span a longer time, typically several phones, thereby having more stable acoustic realizations. In this work, we aim to train a syllable-based acoustic model for Mandarin ASR with lattice-free maximum mutual information (LF-MMI) criterion. We expect that, the combination of longer linguistic units, the RNN-based model structure and the sequence-level objective function, can result in better modeling of long-term temporal acoustic variations. We make multiple modifications to improve the performance of syllable-based AM and benchmark our models on two large-scale databases. Experimental results show that the proposed syllable-based AM performs much better than the CD phone-based baseline, especially on noisy test sets, with faster decoding speed.