Evaluation of sublexical and lexical models of acoustic disfluencies for spontaneous speech recognition in Spanish

Spontaneous speech is full of acoustic disfluencies that rarely appear in read or laboratory speech. A very simple and straightforward approach is presented, in which acoustic disfluences are modelled by augmenting the inventory of sublexical units, which originally consisted of 23 context independent phones plus a special unit for silent pauses. This set was augmented with 12 additional units accounting for lengthenings of sounds, filled pauses and noises. Two speech databases, both in Spanish, were used in the experiments. A phonetically balanced database was used for initializing the acoustic models. A spontaneous speech database consisting of 227 dialogues was used both for training and testing purposes. Recognition rates, in terms of acoustic-phonetic accuracy and word accuracy, with and without filtering acoustic disfluencies prior to alignments, were obtained to evaluate the contribution of these models to speech recognition. Also, some specific but significant examples were explored and discussed. Experimental results showed that using explicit models of acoustic disfluencies clearly improved the performance of a spontaneous speech recognition system.