3 accepted papers
This paper describes neural ConfNet classification, a novel fully neural network based spoken utterance classification method that uses word confusion networks (ConfNets). Our motivation is to establish a spoken utterance classification method that can precisely understand natural language and robus…
Constructing deep neural network (DNN) acoustic models from limited training data is an important issue for the development of automatic speech recognition (ASR) applications that will be used in various application-specific acoustic environments. To this end, domain adaptation techniques that train…
Parallel phonetically aware deep neural networks (PPA-DNNs) and long short-term memory recurrent neural networks (PPA-LSTM-RNNs) to enhance frame-by-frame discriminative modeling of spoken language identification are proposed. This idea is inspired by traditional systems based on parallel phoneme re…