Acoustic modeling by phoneme templates and modified one-pass DP decoding for continuous speech recognition

We propose a novel framework for continuous speech recognition (CSR) based on non-parametric acoustic modeling using multiple phoneme templates set in a modified one-pass DP decoding algorithm, in contrast to the conventional HMM acoustic models set in Viterbi decoding. We particularly emphasis the 'selectivity' property of templates as set in the proposed modified one-pass DP decoding algorithm and explore various contextual definitions of the templates and their relative performances for a range of small vocabulary tasks with TIMIT database using only acoustic models. Based on this, we show that the proposed framework based on phoneme template modeling is a viable means for CSR with potential for interesting issues in acoustic modeling and decoding strategies, particularly in the paradigmatically novel framework of model-free CSR.