Evaluating unsupervised data in isolated speech recognizer

This paper presents initial studies of applying isolated speech recognizer (ISR) on different datasets of adultpsilas speech ISR is normally created for a targeted user or language. Even though the targeted user is defined, there are often speakers who are badly recognized. The purpose of this study is to determine whether a different recording specification has an effect on the performance of the recognizer. We used probabilistic models, known as hidden Markov models (HMMs) to interpret a sequence of word. In this study, we tested four datasets that consist of isolated word that utter the different days in standard Malay language. We apply the ISR on the datasets to determine the recognition rate performance and identify pattern of word recognition. The overall result shows that there is strong correlation between the different specification of the datasets and recognition rate. The study shows that, if certain specification is not fully considered during the recording, the recognition rate by the ISR is degraded.