5 accepted papers
Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) senso…
This paper describes a new architecture of deep neural networks (DNNs) for acoustic models. Training DNNs from raw speech signals will provide 1) novel features of signals, 2) normalization-free processing such as utterance-wise mean subtraction, and 3) low-latency speech recognition for robot audit…
This paper describes an unsupervised adaptation method of deep neural networks (DNNs) regarding discriminative sound source localization (SSL). DNNs-based SSL and its unsupervised adaptation fail under different conditions from those during training. The estimations sometimes include incoherent unpr…
This paper describes an unsupervised method of adapting deep neural networks (DNNs) for sound source localization (SSL). DNNs-based SSL achieves high localization accuracy for sound data that are similar to training data. However, the accuracy deteriorates if a sound source is at an unknown position…
This paper describes sound source localization (SSL) based on deep neural networks (DNNs) using discriminative training. A naïve DNNs for SSL can be configured as follows. Input is the frequency-domain feature used in other SSL methods, and the structure of DNNs is a fully-connected network using re…