3,566 research outputs found
FPGA Implementation of Spectral Subtraction for In-Car Speech Enhancement and Recognition
The use of speech recognition in noisy environments requires the use of speech enhancement algorithms in order to improve recognition performance. Deploying these enhancement techniques requires significant engineering to ensure algorithms are realisable in electronic hardware. This paper describes the design decisions and process to port the popular spectral subtraction algorithm to a Virtex-4 field-programmable gate array (FPGA) device. Resource analysis shows the final design uses only 13% of the total available FPGA resources. Waveforms and spectrograms presented support the validity of the proposed FPGA design
Realistic multi-microphone data simulation for distant speech recognition
The availability of realistic simulated corpora is of key importance for the
future progress of distant speech recognition technology. The reliability,
flexibility and low computational cost of a data simulation process may
ultimately allow researchers to train, tune and test different techniques in a
variety of acoustic scenarios, avoiding the laborious effort of directly
recording real data from the targeted environment.
In the last decade, several simulated corpora have been released to the
research community, including the data-sets distributed in the context of
projects and international challenges, such as CHiME and REVERB. These efforts
were extremely useful to derive baselines and common evaluation frameworks for
comparison purposes. At the same time, in many cases they highlighted the need
of a better coherence between real and simulated conditions.
In this paper, we examine this issue and we describe our approach to the
generation of realistic corpora in a domestic context. Experimental validation,
conducted in a multi-microphone scenario, shows that a comparable performance
trend can be observed with both real and simulated data across different
recognition frameworks, acoustic models, as well as multi-microphone processing
techniques.Comment: Proc. of Interspeech 201
DNN-Based Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
Multi-frame approaches for single-microphone speech enhancement, e.g., the
multi-frame minimum-variance-distortionless-response (MVDR) filter, are able to
exploit speech correlations across neighboring time frames. In contrast to
single-frame approaches such as the Wiener gain, it has been shown that
multi-frame approaches achieve a substantial noise reduction with hardly any
speech distortion, provided that an accurate estimate of the correlation
matrices and especially the speech interframe correlation vector is available.
Typical estimation procedures of the correlation matrices and the speech
interframe correlation (IFC) vector require an estimate of the speech presence
probability (SPP) in each time-frequency bin. In this paper, we propose to use
a bi-directional long short-term memory deep neural network (DNN) to estimate a
speech mask and a noise mask for each time-frequency bin, using which two
different SPP estimates are derived. Aiming at achieving a robust performance,
the DNN is trained for various noise types and signal-to-noise ratios.
Experimental results show that the multi-frame MVDR in combination with the
proposed data-driven SPP estimator yields an increased speech quality compared
to a state-of-the-art model-based estimator
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
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