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.