Two upper Palaeocene-lower Eocene stratigraphic sequences at the Kharga Oasis (Umm El Ghanayim and Naqb Assiut sections) were studied biostratigraphically on the basis of their calcareous nannofossil content. The investigated interval includes the upper part of the Tarawan Formation, the Tarawan Chalk, and the Esna formations. A total number of sixty-seven different taxa have been identified. The lowest occurrence (LO) of Discoaster araneus was used to place the base of the NP9b Subzone (base of Eocene) at the Gabal Umm El Ghanayim section. The lowest occurrences (LOs) of Rhomboaster bitrifida, Discoaster araneus and D. anartios are used to define the NP9a/NP9b subzonal boundary at the Gabal Naqb Assiut section. In this section, the P/E boundary is marked by a minor lithologic hiatus as indicated by the absence of the basal part of the Dababiya Member. At the studied two sections, a major turnover in calcareous nannofossil assemblages across the P/E transition was documented. The abundance of warm water Ericsonia subpertusa, Fasculithus spp., Coccolithus eopelagicus, Discoaster spp., Rhomboaster bitrifida and Tribrachiatus bramlettei characterize the Palaeocene-Eocene transition and suggest global warming and the Palaeocene-Eocene Thermal Maximum (PETM). 2.2. Esna Formation The term Esna Shale was first introduced by BEADNELL (1905), to describe the shale succession that overlies the Tarawan Forma tion and underlies the Thebes Formation at Gabal Oweina, Esna Idfu area, Upper Nile Valley. ABDELRAZIK (1972) separated this formation into two members, the El Hanadi Member at the base and the El Shaghab Member at the top. DUPUIS et al. (2003) classified the Esna Formation from the base to the top, into three units (Esna Unit 1, Esna Unit 2 and Esna Unit 3) (Table 1).
In this research, a novel feature set is used to automatically segment speech signal. Automatic segmentation is very useful especially for large database. A hybrid features model is created from wavelet packet analysis and mel-scale is used to train Hidden Markov Model (HMM) for phone boundary detection. HMM is implemented using the Hidden Markov Model Toolkit (HTK).The database (Ked-TIMIT) is used for result verifications and Mel Frequency Cepstral Coefficients (MFCC) is used as reference for evaluating the results of the proposed Hybrid model. The results are categorized for vowels, consonants and short phones. Phone duration and start location are used as metrics to evaluate the system success rate. Success rate of 74% is achieved for consonant detection, 72% for vowel detection and 58% for short phone detection. Using the simple metric that relies only on boundary locations but ignoring duration, the achieved results are 92.5% for consonant detection, 90% for vowel detection and 77.5% for short phoneme detection. In addition to boundary detection the proposed hybrid model is utilized to compare newly developed features called Mel scale Best Tree Encoding (Mel-BTE ) to the mostly used popular features MFCC along with all experiments using the same database. The relative results forMel-BTE with respect to MFCC are 94.77% for consonant detection, 87.5% for vowel detection and 93.33% for short phoneme detection.
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