Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Giacobello, Daniele; Wung, Jason; Pichevar, Ramin; Atkins, Joshua

doi:10.1109/hscma.2014.6843252

Cited by 6 publications

(9 citation statements)

References 32 publications

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“…To reduce the time and effort required for manual tuning, automatic tuning for speech enhancement has been proposed [13], [14]. Speech enhancement tuning is treated as an optimization problem, where optimal tuning parameter values are determined automatically using a training set.…”

Section: Related Work a Algorithm Tuning Approachesmentioning

confidence: 99%

“…In [14], a methodology was presented for tuning speech enhancement algorithms automatically, employing the genetic algorithm to solve the tuning optimization problem. Their tuning methodology was employed for a single-channel system for hands-free devices.…”

Section: Related Work a Algorithm Tuning Approachesmentioning

confidence: 99%

“…The ultimate goal of tuning is to adapt speech-enabled systems to real-life conditions where the characteristics of the noise might be changing constantly. Hence, the parameters need to be updated continuously over time, which can be done by using tuning algorithms such as those first proposed in [14]. The first premise for effectively achieving this tuning is to involve an enhancement technique with parameters that are accessible, quantifiable, and easy to modify.…”

Section: Tuning Of Mbss-and Gs-based Speech Enhancement Techniquesmentioning

confidence: 99%

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A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

LeBlanc

Selouani

2022

IEEE Access

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This paper presents a novel self-adaptive approach for speech enhancement in the context of highly nonstationary noise. A two-stage deep neuroevolutionary technique for speech enhancement is proposed. The first stage is composed of a deep neural network (DNN) method for speech enhancement. Two DNN methods were tested at this stage, namely, both a deep complex convolution recurrent network (DCCRN) and a residual long short-term memory neural network (ResLSTM). The ResLSTM method was combined with a minimum mean-square error method to perform a preliminary enhancement. The ResLSTM network is used as an a priori signal-to-noise ratio (SNR) estimator. The second stage implements a self-adaptive multiband spectral subtraction enhancement method using tuning optimization based on a genetic algorithm. The proposed two-stage technique is evaluated using objective measures of speech quality and intelligibility. The experiments are carried out using the NOIZEUS noisy speech corpus using conditions of real-world stationary, colored, and nonstationary noise sources at multiple SNR levels. These experiments demonstrate the advantage of building a cooperative approach using evolutionary and deep learning-based techniques that are capable of achieving robust speech enhancement in adverse conditions. Indeed, the experimental tests show that the proposed two-stage technique outperformed a baseline implementation using a state-of-the-art deep learning approach by an average 13% and 6% improvement for six noise conditions at a −5 dB and a 0 dB input SNR, respectively.

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Section: Related Work a Algorithm Tuning Approachesmentioning

confidence: 99%

Section: Related Work a Algorithm Tuning Approachesmentioning

confidence: 99%

Section: Tuning Of Mbss-and Gs-based Speech Enhancement Techniquesmentioning

confidence: 99%

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A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

LeBlanc

Selouani

2022

IEEE Access

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“…We evaluated the performance of the methods presented simulating artificial utterances that mimic real use cases specific for voice enabled smart speakers. Similarly to the experiments presented in [28,29], we use a room configuration generator. Our target hardware is known, in particular, we considered a 6-microphone circular array of 72 mm diameter.…”

Section: Experimental Analysismentioning

confidence: 99%

Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction

Giacobello¹,

Jensen

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we consider methods for improving far-field speech recognition using dereverberation based on sparse multi-channel linear prediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparse desired speech signal in the short-term Fourier transform domain, by proposing sparsity promoting convex functions. Furthermore, we show how to improve performance by applying regularization into both the reweighted least squares and convex methods. We evaluate the methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposed convex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly different acoustic scenarios.

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“…Giacobello et al have recently proposed a formal methodology based on a genetic algorithm for tuning the parameters of a singlemicrophone speech enhancement system for handsfree devices. 28 The proposal greatly improves the enhanced speech quality and also speeds up development and deployment time.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Griol

Iglesias

Ledezma

et al. 2016

Int. J. Neur. Syst.

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This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

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Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Cited by 6 publications

References 32 publications

A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

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