文章名稱引用次數(shù)

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• Improving multilingual speech emotion recognition by combining acoustic features in a three-layer model9
• Dysarthric speech classification from coded telephone speech using glottal features8
• End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition8
• Discrimination of L2 Greek vowel contrasts: Evidence from learners with Arabic L1 background7
• Bone-conducted speech enhancement using deep denoising autoencoder6
• Updating the Silent Speech Challenge benchmark with deep learning6
• Measuring communication difficulty through effortful speech production during conversation6
• Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO6