117 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
Environmentally robust ASR front-end for deep neural network acoustic models
This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.csl.2014.11.00
Speech enhancement for robust automatic speech recognition: Evaluation using a baseline system and instrumental measures
Automatic speech recognition in everyday environments must be robust to significant levels of reverberation and noise. One strategy to achieve such robustness is multi-microphone speech enhancement. In this study, we present results of an evaluation of different speech enhancement pipelines using a state-of-the-art ASR system for a wide range of reverberation and noise conditions. The evaluation exploits the recently released ACE Challenge database which includes measured multichannel acoustic impulse responses from 7 different rooms with reverberation times ranging from 0.33 s to 1.34 s. The reverberant speech is mixed with ambient, fan and babble noise recordings made with the same microphone setups in each of the rooms. In the first experiment performance of the ASR without speech processing is evaluated. Results clearly indicate the deleterious effect of both noise and reverberation. In the second experiment, different speech enhancement pipelines are evaluated with relative word error rate reductions of up to 82%. Finally, the ability of selected instrumental metrics to predict ASR performance improvement is assessed. The best performing metric, Short-Time Objective Intelligibility Measure, is shown to have a Pearson correlation coefficient of 0.79, suggesting that it is a useful predictor of algorithm performance in these tests
Efficient and Robust Methods for Audio and Video Signal Analysis
This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification.Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process.Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR.The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance
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
Multichannel Online Dereverberation based on Spectral Magnitude Inverse Filtering
This paper addresses the problem of multichannel online dereverberation. The
proposed method is carried out in the short-time Fourier transform (STFT)
domain, and for each frequency band independently. In the STFT domain, the
time-domain room impulse response is approximately represented by the
convolutive transfer function (CTF). The multichannel CTFs are adaptively
identified based on the cross-relation method, and using the recursive least
square criterion. Instead of the complex-valued CTF convolution model, we use a
nonnegative convolution model between the STFT magnitude of the source signal
and the CTF magnitude, which is just a coarse approximation of the former
model, but is shown to be more robust against the CTF perturbations. Based on
this nonnegative model, we propose an online STFT magnitude inverse filtering
method. The inverse filters of the CTF magnitude are formulated based on the
multiple-input/output inverse theorem (MINT), and adaptively estimated based on
the gradient descent criterion. Finally, the inverse filtering is applied to
the STFT magnitude of the microphone signals, obtaining an estimate of the STFT
magnitude of the source signal. Experiments regarding both speech enhancement
and automatic speech recognition are conducted, which demonstrate that the
proposed method can effectively suppress reverberation, even for the difficult
case of a moving speaker.Comment: Paper submitted to IEEE/ACM Transactions on Audio, Speech and
Language Processing. IEEE Signal Processing Letters, 201
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