Practical Issues of Building Robust HMM Models Using HTK and SPHINX Systems

Abstract

For a couple of decades there has been a great effort spent to build and employ ASR systems in areas like information retrieval systems, dialog systems, etc., but only as the technology has evolved further other applications like dictation systems or even automatic transcription of natural speech (Nouza et al., 2005) are emerging. These advanced systems should be capable to operate on a real time base, must be speaker independent, reaching high accuracy and support dictionaries containing several hundreds of thousands of words. These strict requirements can be currently met by HMM models of tied context dependent (CD) phonemes with multiple Gaussian mixtures, which is a technique known from the 60ties (Baum & Eagon, 1967). As this statistical concept is mathematically tractable it, unfortunately, doesn’t completely reflect the physical underlying process. Therefore soon after its creation there have been lot of attempts to alleviate that. Nowadays the classical concept of HMM has evolved into areas like hybrid solutions with neural networks, utilisation of different than ML or MAP training strategies that minimize recognition errors by the means of corrective training, maximizing mutual information (Huang et. al., 1990) or by constructing large margin HMMs (Jiang & Li, 2007). Furthermore, a few methods have been designed and tested aiming to suppress the first order Markovian restriction by e.g. explicitly modelling the time duration (Levinson, 1986), splitting states into more complex structures (Bonafonte et al., 1996), using double (Casar & Fonollosa, 2007) or multilayer structures of HMM. Another vital issue is the robust and accurate feature extraction method. Again this matter is not fully solved and various popular features and techniques exist like: MFCC and CLPC coefficients, PLP features, TIFFING (Nadeu & Macho, 2001), RASTA filter (Hermasky & Morgan, 1994), etc. Even despite the huge variety of advanced solutions many of them are either not general enough or are rather impractical for the real-life employment. Thus most of the currently employed systems are based on continuous context independent (CI) or tied CD HMM models of phonemes with multiple Gaussian mixtures trained by ML or MAP criteria. As there is no analytical solution of this task, the training process must be an iterative one (Huang et al., 1990). Unfortunately, there is no guarantee of reaching local maxima, thus lot of effort is paid to the training phase in which many stages are involved. Thus there are some complex systems that allow convenient and flexible training of HMM models, where the most famous are HTK and SPHINX.