My SLT 2018 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.

Create a login based on your email (takes less than one minute)
Perform 'Paper Search'
Select papers that you desire to save in your personalized schedule
Click on 'My Schedule' to see the current list of selected papers
Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Presentation #	19
Session:	ASR IV
Session Time:	Friday, December 21, 13:30 - 15:30
Presentation Time:	Friday, December 21, 13:30 - 15:30
Presentation:	Poster
Topic:	Speech recognition and synthesis:
Paper Title:	RAPID SPEAKER ADAPTATION OF NEURAL NETWORK BASED FILTERBANK LAYER FOR AUTOMATIC SPEECH RECOGNITION
Authors:	Hiroshi Seki; Toyohashi University of Technology
	Kazumasa Yamamoto; Chubu University
	Tomoyosi Akiba; Toyohashi University of Technology
	Seiichi Nakagawa; Chubu University
Abstract:	Deep neural networks (DNN) have achieved significant success in the field of automatic speech recognition. Previously, we proposed a filterbank-incorporated DNN which takes power spectra as input features. This method has a function of VTLN (Vocal tract length normalization) and fMLLR (feature-space maximum likelihood linear regression). The filterbank layer can be implemented by using a small number of parameters and is optimized under a framework of backpropagation. Therefore, it is advantageous in adaptation under limited available data. In this paper, speaker adaptation is applied to the filterbank-incorporated DNN. By applying speaker adaptation using 15 utterances, the adapted model gave a 7.4% relative improvement in WER over the baseline DNN at a significance level of 0.005 on CSJ task. Adaptation of filterbank layer also showed better performance than the other adaptation methods; singular value decomposition (SVD) based adaptation and learning hidden unit contributions (LHUC).