Evaluating raw waveforms with deep learning frameworks for speech emotion recognition

Speech emotion recognition is a challenging task in speech processing field. For this reason, feature extraction process has a crucial importance to demonstrate and process the speech signals. In this work, we represent a model, which feeds raw audio files directly into the deep neural networks without any feature extraction stage for the recognition of emotions utilizing six different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To demonstrate the contribution of proposed model, the performance of traditional feature extraction techniques namely, mel-scale spectogram, mel-frequency cepstral coefficients, are blended with machine learning algorithms, ensemble learning methods, deep and hybrid deep learning techniques. Support vector machine, decision tree, naive Bayes, random forests models are evaluated as machine learning algorithms while majority voting and stacking methods are assessed as ensemble learning techniques. Moreover, convolutional neural networks, long short-term memory networks, and hybrid CNN- LSTM model are evaluated as deep learning techniques and compared with machine learning and ensemble learning methods. To demonstrate the effectiveness of proposed model, the comparison with state-of-the-art studies are carried out. Based on the experiment results, CNN model excels existent approaches with 95.86% of accuracy for TESS+RAVDESS data set using raw audio files, thence determining the new state-of-the-art. The proposed model performs 90.34% of accuracy for EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model, 85.76% of accuracy for SAVEE with CNN model in speaker-independent audio categorization problems.Comment: 14 pages, 6 Figures, 8 Table

Yumurtacı japon bıldırcınlarında diyete ilave edilen zeytin yaprağı özütü ve zarlı yumurta kabuğunun performans, yumurta kalitesi, kan biyokimyasal ve kemik parametreleri üzerine etkileri

This study was conducted to determine the effects of dietary supplementation of olive leaf extract (OLE), eggshell with the membrane (ESM), and the ESM that absorbed the OLE (OLE+ESM) on the performance, egg quality, biochemical, and bone parameters in laying Japanese quail. A total of 112 quail, being 45-day-old, were divided into 4 groups with 4 replicates. The quail were fed with four diets: i) basal diet ii) basal diet supplemented with 400 ppm OLE iii) basal diet supplemented with 2% ESM, and iv) basal diet supplemented with 2% ESM that absorbed with 400 ppm OLE. Egg weight was observed to be higher in the OLE group (P0.05). There was no statistical difference in tibia bone parameters (P>0.05). The lowest concentration of serum lactate dehydrogenase (LDH) was observed in control group (P<0.01). Serum uric acid level decreased in ESM group (P<0.01). OLE supplementation had limited impacts on quail nutrition. Consequently, while the individual usage of OLE and ESM did not show remarkable effects, the mixture of OLE and ESM has been found to positively affect the egg quality and performance parameters