1,127 research outputs found
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders
We show that a Modular Neural Network (MNN) can combine various speech
enhancement modules, each of which is a Deep Neural Network (DNN) specialized
on a particular enhancement job. Differently from an ordinary ensemble
technique that averages variations in models, the propose MNN selects the best
module for the unseen test signal to produce a greedy ensemble. We see this as
Collaborative Deep Learning (CDL), because it can reuse various already-trained
DNN models without any further refining. In the proposed MNN selecting the best
module during run time is challenging. To this end, we employ a speech
AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as
similar as possible if its input is clean speech. Therefore, the AE can gauge
the quality of the module-specific denoised result by seeing its AE
reconstruction error, e.g. low error means that the module output is similar to
clean speech. We propose an MNN structure with various modules that are
specialized on dealing with a specific noise type, gender, and input
Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always
works better than an arbitrarily chosen DNN module and sometimes as good as an
oracle result
Single-Microphone Speech Enhancement and Separation Using Deep Learning
The cocktail party problem comprises the challenging task of understanding a
speech signal in a complex acoustic environment, where multiple speakers and
background noise signals simultaneously interfere with the speech signal of
interest. A signal processing algorithm that can effectively increase the
speech intelligibility and quality of speech signals in such complicated
acoustic situations is highly desirable. Especially for applications involving
mobile communication devices and hearing assistive devices. Due to the
re-emergence of machine learning techniques, today, known as deep learning, the
challenges involved with such algorithms might be overcome. In this PhD thesis,
we study and develop deep learning-based techniques for two sub-disciplines of
the cocktail party problem: single-microphone speech enhancement and
single-microphone multi-talker speech separation. Specifically, we conduct
in-depth empirical analysis of the generalizability capability of modern deep
learning-based single-microphone speech enhancement algorithms. We show that
performance of such algorithms is closely linked to the training data, and good
generalizability can be achieved with carefully designed training data.
Furthermore, we propose uPIT, a deep learning-based algorithm for
single-microphone speech separation and we report state-of-the-art results on a
speaker-independent multi-talker speech separation task. Additionally, we show
that uPIT works well for joint speech separation and enhancement without
explicit prior knowledge about the noise type or number of speakers. Finally,
we show that deep learning-based speech enhancement algorithms designed to
minimize the classical short-time spectral amplitude mean squared error leads
to enhanced speech signals which are essentially optimal in terms of STOI, a
state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page
Visual Speech Enhancement
When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network. We include in the training data videos to which we added the voice of
the target speaker as background noise. Since the audio input is not sufficient
to separate the voice of a speaker from his own voice, the trained model better
exploits the visual input and generalizes well to different noise types. The
proposed model outperforms prior audio visual methods on two public lipreading
datasets. It is also the first to be demonstrated on a dataset not designed for
lipreading, such as the weekly addresses of Barack Obama.Comment: Accepted to Interspeech 2018. Supplementary video:
https://www.youtube.com/watch?v=nyYarDGpcY
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
In this paper we propose the utterance-level Permutation Invariant Training
(uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning
based solution for speaker independent multi-talker speech separation.
Specifically, uPIT extends the recently proposed Permutation Invariant Training
(PIT) technique with an utterance-level cost function, hence eliminating the
need for solving an additional permutation problem during inference, which is
otherwise required by frame-level PIT. We achieve this using Recurrent Neural
Networks (RNNs) that, during training, minimize the utterance-level separation
error, hence forcing separated frames belonging to the same speaker to be
aligned to the same output stream. In practice, this allows RNNs, trained with
uPIT, to separate multi-talker mixed speech without any prior knowledge of
signal duration, number of speakers, speaker identity or gender. We evaluated
uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks
and found that uPIT outperforms techniques based on Non-negative Matrix
Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and
compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network
(DANet). Furthermore, we found that models trained with uPIT generalize well to
unseen speakers and languages. Finally, we found that a single model, trained
with uPIT, can handle both two-speaker, and three-speaker speech mixtures
Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments
In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility
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