Discriminatively estimated discrete, parametric and smoothed-discrete duration models for speech recognition

Duration of phonemic segments provide important cues for distinguishing words in languages such as Arabic. Recently, we proposed a discriminatively estimated joint acoustic, duration and language model for large vocabulary speech recognition. In that work, we found simple discrete models to be effective for modeling duration, albeit they were neither smoothed nor parsimonious. These limitations are ad dressed here with two alternative models parametric and smoothed-discrete models. Unlike previous work on para metric duration model, we estimate their parameters discriminatively and derive an analytical expression for estimating the parameters of a log-normal distribution using a recent approach. On a large vocabulary Arabic task, we empirically evaluated different segmental units and durations models. Our results show bigrams of clustered states modeled with smoothed-discrete duration models are relatively more accurate and efficient than other models considered.