… speech recognizer using deep neural networks is proposed as model to predict speech
intelligibility … recognition (ASR) system is employed that uses a deep neural network (DNN) to …

… speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we …
achieving large improvements in estimated speech quality (SQ) and speech intelligibility (SI), when …

… our first attempt at modeling speech intelligibility using deep neural networks. We are …
Deep neural nets often require large amounts of training data to achieve high accuracy and …

… speech to make predictions. In this paper, we investigate the use of Convolutional Neural
Networks (… This demonstrates the potential of deep learning for speech intelligibility prediction. …

… the speech intelligibility of our DNNbased speech enhancement system. On the other hand,
STOI correctly predicts that GVN has no significant effect on speech intelligibility. According …

… mate the speech intelligibility and compared the estimation accuracy of several machine
learning … In this paper, we attempt to introduce deep neural networks (DNN) to this task. Initial …

… This paper presents a speech intelligibility model based on automatic speech recognition
(ASR), combining phoneme probabilities from deep neural networks (DNN) and a …

… denoising autoencoder SE method, FCN(S) can obtain better gain in the speech intelligibility
for normal as well as vocoded speech. This study, being the first to evaluate deep learning …

… Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to
maximize an approximation of the Short-Time Objective Intelligibility(… speech intelligibility gain …

… In our experiments, we find that the tunable hyper-parameter λ affects speech intelligibility
and quality of the enhanced speech. Currently, we are using a fixed value during system …