21 research outputs found
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
CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
Convolution-augmented transformers (Conformers) are recently proposed in
various speech-domain applications, such as automatic speech recognition (ASR)
and speech separation, as they can capture both local and global dependencies.
In this paper, we propose a conformer-based metric generative adversarial
network (CMGAN) for speech enhancement (SE) in the time-frequency (TF) domain.
The generator encodes the magnitude and complex spectrogram information using
two-stage conformer blocks to model both time and frequency dependencies. The
decoder then decouples the estimation into a magnitude mask decoder branch to
filter out unwanted distortions and a complex refinement branch to further
improve the magnitude estimation and implicitly enhance the phase information.
Additionally, we include a metric discriminator to alleviate metric mismatch by
optimizing the generator with respect to a corresponding evaluation score.
Objective and subjective evaluations illustrate that CMGAN is able to show
superior performance compared to state-of-the-art methods in three speech
enhancement tasks (denoising, dereverberation and super-resolution). For
instance, quantitative denoising analysis on Voice Bank+DEMAND dataset
indicates that CMGAN outperforms various previous models with a margin, i.e.,
PESQ of 3.41 and SSNR of 11.10 dB.Comment: 16 pages, 10 figures and 5 tables. arXiv admin note: text overlap
with arXiv:2203.1514
Deep learning for speech enhancement : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
Speech enhancement, aiming at improving the intelligibility and overall perceptual quality of a contaminated speech signal, is an effective way to improve speech communications. In this thesis, we propose three novel deep learning methods to improve speech enhancement performance.
Firstly, we propose an adversarial latent representation learning for latent space exploration of generative adversarial network based speech enhancement. Based on adversarial feature learning, this method employs an extra encoder to learn an inverse mapping from the generated data distribution to the latent space. The encoder establishes an inner connection with the generator and contributes to latent information learning.
Secondly, we propose an adversarial multi-task learning with inverse mappings method for effective speech representation. This speech enhancement method focuses on enhancing the generator's capability of speech information capture and representation learning. To implement this method, two extra networks are developed to learn the inverse mappings from the generated distribution to the input data domains.
Thirdly, we propose a self-supervised learning based phone-fortified method to improve specific speech characteristics learning for speech enhancement. This method explicitly imports phonetic characteristics into a deep complex convolutional network via a contrastive predictive coding model pre-trained with self-supervised learning.
The experimental results demonstrate that the proposed methods outperform previous speech enhancement methods and achieve state-of-the-art performance in terms of speech intelligibility and overall perceptual quality
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Spatial features of reverberant speech: estimation and application to recognition and diarization
Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome human-machine interactions.This thesis proposes a method to non-intrusively estimate room acoustic parameters, paying special attention to a room acoustic parameter highly correlated with speech recognition degradation: clarity index. In addition, a method to provide information regarding the estimation accuracy is proposed. An analysis of the phoneme recognition performance for multiple reverberant environments is presented, from which a confusability metric for each phoneme is derived. This confusability metric is then employed to improve reverberant speech recognition performance. Additionally, room acoustic parameters can as well be used in speech recognition to provide robustness against reverberation. A method to exploit clarity index estimates in order to perform reverberant speech recognition is introduced.
Finally, room acoustic parameters can also be used to diarize reverberant speech. A room acoustic parameter is proposed to be used as an additional source of information for single-channel diarization purposes in reverberant environments. In multi-channel environments, the time delay of arrival is a feature commonly used to diarize the input speech, however the computation of this feature is affected by reverberation. A method is presented to model the time delay of arrival in a robust manner so that speaker diarization is more accurately performed.Open Acces
Relating EEG to continuous speech using deep neural networks: a review
Objective. When a person listens to continuous speech, a corresponding
response is elicited in the brain and can be recorded using
electroencephalography (EEG). Linear models are presently used to relate the
EEG recording to the corresponding speech signal. The ability of linear models
to find a mapping between these two signals is used as a measure of neural
tracking of speech. Such models are limited as they assume linearity in the
EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an
alternative, deep learning models have recently been used to relate EEG to
continuous speech, especially in auditory attention decoding (AAD) and
single-speech-source paradigms. Approach. This paper reviews and comments on
deep-learning-based studies that relate EEG to continuous speech in AAD and
single-speech-source paradigms. We point out recurrent methodological pitfalls
and the need for a standard benchmark of model analysis. Main results. We
gathered 29 studies. The main methodological issues we found are biased
cross-validations, data leakage leading to over-fitted models, or
disproportionate data size compared to the model's complexity. In addition, we
address requirements for a standard benchmark model analysis, such as public
datasets, common evaluation metrics, and good practices for the match-mismatch
task. Significance. We are the first to present a review paper summarizing the
main deep-learning-based studies that relate EEG to speech while addressing
methodological pitfalls and important considerations for this newly expanding
field. Our study is particularly relevant given the growing application of deep
learning in EEG-speech decoding