Continuous Bangla Speech Segmentation using Short-term Speech Features Extraction Approaches

This paper presents simple and novel feature extraction approaches for segmenting continuous Bangla speech sentences into words/sub-words. These methods are based on two simple speech features, namely the time-domain features and the frequency-domain features. The time-domain features, such as short-time signal energy, short-time average zero crossing rate and the frequency-domain features, such as spectral centroid and spectral flux features are extracted in this research work. After the feature sequences are extracted, a simple dynamic thresholding criterion is applied in order to detect the word boundaries and label the entire speech sentence into a sequence of words/sub-words. All the algorithms used in this research are implemented in Matlab and the implemented automatic speech segmentation system achieved segmentation accuracy of 96%.

@article{Rahman2012,
  title     = {Continuous Bangla Speech Segmentation using Short-term Speech Features Extraction Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {11},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Md Mijanur Rahman and Md. Al-Amin Bhuiyan},
  doi       = {10.14569/IJACSA.2012.031121},
  url       = {https://doi.org/10.14569/IJACSA.2012.031121}
}