Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Observation uncertainty measures for sparse imputation

status: publishe

Unsupervised feature extraction for multimedia event detection and ranking using audio content

ABSTRACT In this paper, we propose a new approach to classify and rank multimedia events based purely on audio content using video data from TRECVID-2013 multimedia event detection (MED) challenge. We perform several layers of nonlinear mappings to extract a set of unsupervised features from an initial set of temporal and spectral features to obtain a superior presentation of the atomic audio units. Additionally, we propose a novel weighted divergence measure for kernel based classifiers. The extensive set of experiments confirms that augmentation of the proposed steps results in an improved accuracy for most of the event classes

Uncertainty measures for improving exemplar-based source separation

status: publishe