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Phonetic Segmentation using a Wavelet-based Speech Cepstral Features and Sparse Representation Classifier

Al-Hassani Ihsan, Al-Dakkak Oumayma, Assami Abdlnaser

Journal of Telecommunications and Information Technology

2021

nr 4

12--22

Speech segmentation is the process of dividing speech signal into distinct acoustic blocks that could be words, syllables or phonemes. Phonetic segmentation is about finding the exact boundaries for the different phonemes that composes a specific speech signal. This problem is crucial for many applications, i.e. automatic speech recognition (ASR). In this paper we propose a new model-based text independent phonetic segmentation method based on wavelet packet speech parametrization features and using the sparse representation classifier (SRC). Experiments were performed on two datasets, the first is an English one derived from TIMIT corpus, while the second is an Arabic one derived from the Arabic speech corpus. Results showed that the proposed wavelet packet decomposition features outperform the MFCC features in speech segmentation task, in terms of both F1-score and R-measure on both datasets. Results also indicate that the SRC gives higher hit rate than the famous k-Nearest Neighbors (k-NN) classifier on TIMIT dataset.

Adaptive wavelet techniques for compressing digital mammograms

Bruckmann A., Flatscher H., Schell T., Uhl A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

1999

Vol. 3, No 4

381-396

In this work we investigate the possible benefit of employing adaptive wavelet algorithms instead of the classical fixed pyramidal wavelet decomposition for the compression of digital mammograms. In particular, we target on adaptive wavelet packet and NSMRA decompositions. We observe that information cost function optimized wavelet packet subband structures do not offer compression performance gain in this case whereas NSMRA decompositions moderately improve the results of classical wavelet decompositions. Due to the lack of fast and reliable search algorithms fixed NSMRA decompositions need to be generated and employed for classes of similar images.