A comparative study on phonological feature detection from continuous speech with respect to variable corpus size

In this paper, place and manner of articulation based phonological features have been successfully identified with high accuracy using very minimal amount of training data. In detection-based, bottom-up speech recognition approach, the phonological feature based acoustic-phonetic speech attributes are considered as a key component. After identifying the features, they are merged together to get the phonemes. So this type of feature detection using low corpus size shows a path with which continuous speech can be recognized using inadequate data repository also. To execute the experiment, both the language, Bengali and English have been considered. The sentences were trained using deep neural network. Training procedure is carried out for Bengali using three different corpus sizes with a number of 100, 200, and 500 sentences. The average frame level accuracies were obtained as 87.88%, 88.43% and 88.96% respectively for CDAC speech corpus. Whereas using the same training procedure for TIMIT corpus, the accuracies were 87.97%, 88.84%, and 89.39% respectively. So the average frame level accuracy is almost same irrespective of number of training data. This ensures, in case of small speech corpora, phonological feature based speech attributes can be detected with the bottom-up approach.