68 research outputs found
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
Reverberation, which is generally caused by sound reflections from walls,
ceilings, and floors, can result in severe performance degradation of acoustic
applications. Due to a complicated combination of attenuation and time-delay
effects, the reverberation property is difficult to characterize, and it
remains a challenging task to effectively retrieve the anechoic speech signals
from reverberation ones. In the present study, we proposed a novel integrated
deep and ensemble learning algorithm (IDEA) for speech dereverberation. The
IDEA consists of offline and online phases. In the offline phase, we train
multiple dereverberation models, each aiming to precisely dereverb speech
signals in a particular acoustic environment; then a unified fusion function is
estimated that aims to integrate the information of multiple dereverberation
models. In the online phase, an input utterance is first processed by each of
the dereverberation models. The outputs of all models are integrated
accordingly to generate the final anechoic signal. We evaluated the IDEA on
designed acoustic environments, including both matched and mismatched
conditions of the training and testing data. Experimental results confirm that
the proposed IDEA outperforms single deep-neural-network-based dereverberation
model with the same model architecture and training data
Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition
Denoising autoencoders (DAs) have shown success in gener-ating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltz-mann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by correspond-ing clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recog-nition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25 % absolute im-provement on the recognition accuracy under various SNRs. Index Terms — robust speech recognition, feature denois-ing, denoising autoencoder, deep neural network 1
Deep neural network techniques for monaural speech enhancement: state of the art analysis
Deep neural networks (DNN) techniques have become pervasive in domains such
as natural language processing and computer vision. They have achieved great
success in these domains in task such as machine translation and image
generation. Due to their success, these data driven techniques have been
applied in audio domain. More specifically, DNN models have been applied in
speech enhancement domain to achieve denosing, dereverberation and
multi-speaker separation in monaural speech enhancement. In this paper, we
review some dominant DNN techniques being employed to achieve speech
separation. The review looks at the whole pipeline of speech enhancement from
feature extraction, how DNN based tools are modelling both global and local
features of speech and model training (supervised and unsupervised). We also
review the use of speech-enhancement pre-trained models to boost speech
enhancement process. The review is geared towards covering the dominant trends
with regards to DNN application in speech enhancement in speech obtained via a
single speaker.Comment: conferenc
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