An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. 
In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods. Because the major advantage of deep learning over conventional methods is its representation ability, which is able to produce highly abstract embedding features from utterances, we first pay close attention to deep-learning-based speaker feature extraction, including the inputs, network structures, temporal pooling strategies, and objective functions respectively, which are the fundamental components of many speaker recognition subtasks. Then, we make an overview of speaker diarization, with an emphasis of recent supervised, end-to-end, and online diarization. Finally, we survey robust speaker recognition from the perspectives of domain adaptation and speech enhancement, which are two major approaches of dealing with domain mismatch and noise problems. Popular and recently released corpora are listed at the end of the paper.

Speaker Recognition Based on Deep Learning: An Overview

An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2020.0104

Review of noise removal techniques in ECG signals

Speaker recognition based on deep learning: An overview

Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations.

Denoising of ECG signal by non-local estimation of approximation coefficients in DWT

In the presented work, we explore some of the challenges in recognizing children’s speech on automatic speech recognition (ASR) systems developed using adults’ speech. In such mismatched ASR tasks, a severely degraded recognition performance is observed due to the gross mismatch in the acoustic attributes between those two groups of speakers. Among the various sources of mismatch, we focus on the large differences in the average pitch values across the adult and child speakers in this work. Earlier studies have shown that the Mel-filterbank employed in the feature extraction is not able to smooth out the pitch harmonics sufficiently in particularly for the high-pitched child speakers. As a result of that, the acoustic features derived for the adult and the child speakers turn out to be significantly mismatched. For addressing this problem, we propose a simple technique based on adaptive-liftering for deriving the pitch-robust features. This enables us to reduce the sensitivity of the acoustic features to the gross variations in pitch across the speakers. The proposed features are found to result in improved performance in the context of deep neural network based ASR system. Further with the use of the existing feature normalization techniques, additional gains are noted.

Pitch-Adaptive Front-End Features for Robust Children's ASR.

Assessment of pitch-adaptive front-end signal processing for children’s speech recognition

Transcribing children's speech using acoustic models trained on adults’ speech is very challenging In such conditions, a highly degraded recognition performance is reported due to large mismatch in the acoustic/linguistic attributes of the training and test data The differences in pitch (or fundamental frequency) between the two groups of speakers is one among several mismatch factors Another important mismatch factor is the difference in speaking rates To overcome these two sources of mismatch, prosody modification is explored in this letter Prosody modification is done by using glottal closure instants (GCIs) as anchoring points The GCIs, in turn, are determined using zero-frequency filtering (ZFF) The ZFF-GCI-based prosody modification is fast and results in highly accurate scaling of pitch and speaking rate The experimental evaluations studying the effect of prosody modification resulted in a relative improvement of $50\%$ over the baseline

Syed Shahnawazuddin

Papers

Denoising of ECG signal by non-local estimation of approximation coefficients in DWT

Pitch-Adaptive Front-End Features for Robust Children's ASR.

Assessment of pitch-adaptive front-end signal processing for children’s speech recognition

Effect of Prosody Modification on Children's ASR

Addressing noise and pitch sensitivity of speech recognition system through variational mode decomposition based spectral smoothing