Exploring Ctc-Network Derived Features with Conventional Hybrid System

Abstract

Recently in automatic speech recognition (ASR) a lot of attention has been given to decoding optimization to boost the performance of connectionist temporal classification criterion (CTC) systems in all neural setups. Different from that, we investigated the use of the output of CTC network as input features to traditional HMM/ANN hybrid systems. By doing so, we benefit from the strengths of the CTC network at label discrimination and the highly optimized decoding stack of conventional hybrid systems. In a Switchboard setup, a feedforward network system using our proposed CTC-network derived features with cross-entropy training outperforms a strong CTC baseline by a margin of 5% rel. in word error rate. With the same model, we achieved further improvements of 9% rel. when combining them with bottleneck features. Additionally, we revealed the possible elimination of the blank label during decoding and the alignment relationship between the CTC model and the traditional HMM system.