Abstract
In this paper, we compare a Probabilistic Landmark-Based speech recognition System (LBS) which uses Knowledge-based Acoustic Parameters (APs) as the front-end with an HMMbased recognition system that uses the Mel-Frequency Cepstral Coefficients as its front end. The advantages of LBS based on APs are (1) the APs are normalized for extra-linguistic information, (2) acoustic analysis at different landmarks may be performed with different resolutions and with different APs, (3) LBS outputs multiple acoustic landmark sequences that signal perceptually significant regions in the speech signal, (4) it may be easier to port this system to another language since the phonetic features captured by the APs are universal, and (5) LBS can be used as a tool for uncovering and subsequently understanding variability. LBS also has a probabilistic framework that can be combined with pronunciation and language models in order to make it more scalable to large vocabulary recognition tasks. Index Terms: landmark, speech recognition, acoustic parameters, phonetic features.
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