In this paper we extend attribute-based lattice rescoring to spontaneous speech recognition. This technique is based on two key features: (i) an attribute-based frontend, which consists of a bank of speech attribute detectors followed up by an evidence merger that generates confidence scores (e.g., sub-word posterior probabilities), and (ii) a rescoring module that integrates information generated by the frontend into an existing ASR engine through lattice rescoring. The speech attributes used in this work are phonetic features, such as frication and palatalization. Experimental results on the Switchboard part of the NIST 2000 Hub5 data set demonstrate that the proposed approach outperforms LVCSR systems based on Gaussian mixture model/ hidden Markov model (GMM/HMM) that does not use attribute related information. Furthermore, a small yet promising improvement is also observed when rescoring word-lattices generated by a state-of-the-art ASR system using deep neural networks. Different frontend configuration are investigated and tested.
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