Linear prediction-based dereverberation with very deep convolutional neural networks for reverberant speech recognition

Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation

“…Table II) indicates that for a dataset containing only larger RT60 values between 1s and 3s it is more preferable to increase the model's RF as the number of CBs in the TCN increases up to 42, i.e. (X, R) = (7,6).…”

“…ERs in speech are typically assumed to occur within the first 50 ms after the direct path. SP methodologies for suppressing reverberant content in speech signals range from a number of techniques with the most prominent approaches in recent work using spectral suppression or linear predictive modelling [7], [8].…”

Preliminary Study on Indonesian Word Recognition for Elder Companion Robot

“…Wunarso et al [11] even tried to build another dataset for the Indonesian language in their study. As stated by Park et al [12], deep neural network output really depends on how feature selection is done, and also how pooling and padding are important in improving speech recognition. They also stated that stacking many convolutional layers as they used in their work to create very deep neural networks does not have a great impact on recognition.…”