Combining multiple-sized sub-word units in a speech recognition system using baseform selection
T. Nagarajan, Parthasarathy Vijayalakshmi, Douglas D. O’Shaughnessy · 2006
Abstract A Longer-sized sub-word unit is known to be a better candi-date in the development of a continuous speech recognition sys-tem. However, the basic problem with such units is the data spar-sity. To overcome this problem, researchers have tried to com-bine longer-sized sub-word unit models with phoneme models. Inthis paper, we have considered only frequently occurring syllablesand VC (Vowel + Consonant) units, and phone-sized units (mono-phones and triphones) for the development of a continuous speechrecognition system. Insuch a case, even for a single pronunciationof a word, there can be multiple representational baseforms in thelexicon, each with different-sized units. We show that a consid-erable improvement in recognition performance can be achievedif the baseforms are selected properly. Out of all possible base-forms for a given word in the lexicon, the baseform that maxi-mizes the acoustic likelihood, for possible sub-word unit concate-nations to make a word, alone is considered. In the baseline sys-tems’ word-lexicon, like pure monophone or triphone-based sys-tems, since only the acoustically weaker baseforms are replacedby baseforms with longer-sized units, the resultant performance isguaranteed to be better than that of baseline systems. The prelim-inary experiments carried out on the TIMIT speech corpus showa considerable improvement in the recognition performance overa pure monophone/triphone-based systems when the larger-sizedunits are combined using proper selection of baseforms.