1,199 research outputs found
A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders
Recent studies have explored the use of deep generative models of speech
spectra based of variational autoencoders (VAEs), combined with unsupervised
noise models, to perform speech enhancement. These studies developed iterative
algorithms involving either Gibbs sampling or gradient descent at each step,
making them computationally expensive. This paper proposes a variational
inference method to iteratively estimate the power spectrogram of the clean
speech. Our main contribution is the analytical derivation of the variational
steps in which the en-coder of the pre-learned VAE can be used to estimate the
varia-tional approximation of the true posterior distribution, using the very
same assumption made to train VAEs. Experiments show that the proposed method
produces results on par with the afore-mentioned iterative methods using
sampling, while decreasing the computational cost by a factor 36 to reach a
given performance .Comment: Submitted to INTERSPEECH 201
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
In this work, we investigated the teacher-student training paradigm to train
a fully learnable multi-channel acoustic model for far-field automatic speech
recognition (ASR). Using a large offline teacher model trained on beamformed
audio, we trained a simpler multi-channel student acoustic model used in the
speech recognition system. For the student, both multi-channel feature
extraction layers and the higher classification layers were jointly trained
using the logits from the teacher model. In our experiments, compared to a
baseline model trained on about 600 hours of transcribed data, a relative
word-error rate (WER) reduction of about 27.3% was achieved when using an
additional 1800 hours of untranscribed data. We also investigated the benefit
of pre-training the multi-channel front end to output the beamformed log-mel
filter bank energies (LFBE) using L2 loss. We find that pre-training improves
the word error rate by 10.7% when compared to a multi-channel model directly
initialized with a beamformer and mel-filter bank coefficients for the front
end. Finally, combining pre-training and teacher-student training produces a
WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization
In this paper we address speaker-independent multichannel speech enhancement
in unknown noisy environments. Our work is based on a well-established
multichannel local Gaussian modeling framework. We propose to use a neural
network for modeling the speech spectro-temporal content. The parameters of
this supervised model are learned using the framework of variational
autoencoders. The noisy recording environment is supposed to be unknown, so the
noise spectro-temporal modeling remains unsupervised and is based on
non-negative matrix factorization (NMF). We develop a Monte Carlo
expectation-maximization algorithm and we experimentally show that the proposed
approach outperforms its NMF-based counterpart, where speech is modeled using
supervised NMF.Comment: 5 pages, 2 figures, audio examples and code available online at
https://team.inria.fr/perception/icassp-2019-mvae
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
Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise
Human-robot interaction relies on a noise-robust audio processing module
capable of estimating target speech from audio recordings impacted by
environmental noise, as well as self-induced noise, so-called ego-noise. While
external ambient noise sources vary from environment to environment, ego-noise
is mainly caused by the internal motors and joints of a robot. Ego-noise and
environmental noise reduction are often decoupled, i.e., ego-noise reduction is
performed without considering environmental noise. Recently, a variational
autoencoder (VAE)-based speech model has been combined with a fully adaptive
non-negative matrix factorization (NMF) noise model to recover clean speech
under different environmental noise disturbances. However, its enhancement
performance is limited in adverse acoustic scenarios involving, e.g. ego-noise.
In this paper, we propose a multichannel partially adaptive scheme to jointly
model ego-noise and environmental noise utilizing the VAE-NMF framework, where
we take advantage of spatially and spectrally structured characteristics of
ego-noise by pre-training the ego-noise model, while retaining the ability to
adapt to unknown environmental noise. Experimental results show that our
proposed approach outperforms the methods based on a completely fixed scheme
and a fully adaptive scheme when ego-noise and environmental noise are present
simultaneously.Comment: Accepted to the 2023 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2023
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