15,735 research outputs found
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
In deep neural networks with convolutional layers, each layer typically has
fixed-size/single-resolution receptive field (RF). Convolutional layers with a
large RF capture global information from the input features, while layers with
small RF size capture local details with high resolution from the input
features. In this work, we introduce novel deep multi-resolution fully
convolutional neural networks (MR-FCNN), where each layer has different RF
sizes to extract multi-resolution features that capture the global and local
details information from its input features. The proposed MR-FCNN is applied to
separate a target audio source from a mixture of many audio sources.
Experimental results show that using MR-FCNN improves the performance compared
to feedforward deep neural networks (DNNs) and single resolution deep fully
convolutional neural networks (FCNNs) on the audio source separation problem.Comment: arXiv admin note: text overlap with arXiv:1703.0801
Online Monaural Speech Enhancement Using Delayed Subband LSTM
This paper proposes a delayed subband LSTM network for online monaural
(single-channel) speech enhancement. The proposed method is developed in the
short time Fourier transform (STFT) domain. Online processing requires
frame-by-frame signal reception and processing. A paramount feature of the
proposed method is that the same LSTM is used across frequencies, which
drastically reduces the number of network parameters, the amount of training
data and the computational burden. Training is performed in a subband manner:
the input consists of one frequency, together with a few context frequencies.
The network learns a speech-to-noise discriminative function relying on the
signal stationarity and on the local spectral pattern, based on which it
predicts a clean-speech mask at each frequency. To exploit future information,
i.e. look-ahead, we propose an output-delayed subband architecture, which
allows the unidirectional forward network to process a few future frames in
addition to the current frame. We leverage the proposed method to participate
to the DNS real-time speech enhancement challenge. Experiments with the DNS
dataset show that the proposed method achieves better performance-measuring
scores than the DNS baseline method, which learns the full-band spectra using a
gated recurrent unit network.Comment: Paper submitted to Interspeech 202
Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering
Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals.
This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates.
To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient
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