Histogram of States Based Assistive System for Speech Impairment Due to Neurological Disorders

Chandrakala, S.; Malini, S.; Veni, S. Vishnika

doi:10.1109/tnsre.2021.3125314

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…However, the cSA-based LSTM RNN method utilizes information regarding both the magnitude and phase of the desired signal to calculate the mask function [77]. (2) The LSTM-RNN efficiently utilizes the temporal information after training LSTM RNN architecture represents good generalization ability [77]. Ensemble learning [76] motivates to train small DNNs and connects them to perform a big task rather than training a big model to perform the big task.…”

Section: Tsp (Telecommunication and Signal Processing) [88]mentioning

confidence: 99%

“…The undesired signal includes a speaker signal other than the target speaker, interference, reverberation, and background noises. Automatic voice recognition (to convert speech into text) [1], assisted living (to make appropriate living conditions for older and persons with disabilities) [2], and hearing aids (to improve the hearing capability of the person with hearing loss) [3], and many more are applications of monaural source separation [4]- [9]. Hence, many researchers are interested in working on source separation problems due to their widespread applications.…”

Section: Introductionmentioning

confidence: 99%

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State-of-the-Art Analysis of Deep Learning-Based Monaural Speech Source Separation Techniques

2023

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The monaural speech source separation problem is an important application in the signal processing field. But recent interaction of deep learning algorithms with signal processing achieves remarkable performance improvement for speech source separation problems. This paper explores the numerous state-of-the-art deep learning-based monaural speech source separation algorithms in the timefrequency (T-F), time, and hybrid domains. The motivation, algorithm, and framework of different deep learning models for monaural speech source separation are analyzed. The benchmarked algorithms in the T-F domain can be categorized as deep neural networks (DNN), clustering, permutation, multi-task learning, computational auditory sense analysis (CASA), and phase reconstruction-based techniques, whereas the state-of-the-art time-domain approaches can be categorized as CNN, RNN, multi-scale fusion (MSF), and transformer-based techniques. The end-to-end post filter (E2EPF) is a hybrid algorithm combining T-F and time-domain works to achieve enhanced results. Time-domain models have shown improvement in separation performance compared to the T-F and hybrid domain models with small model sizes. Methods in T-F, time, and hybrid domains are compared using 𝑆𝐷𝑅, 𝑆𝐼 − 𝑆𝐷𝑅, 𝑆𝐼 − 𝑆𝑁𝑅, PESQ, and 𝑆𝑇𝑂𝐼 as quality assessment metrics on some benchmark datasets.

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Section: Tsp (Telecommunication and Signal Processing) [88]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Analysis of Deep Learning-Based Monaural Speech Source Separation Techniques

2023

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“…Vishnika Veni and Chandrakala [54] researched the application of the deep neural network-hidden Markov model and lattice maximum mutual information technique for the successful identification of damaged speech. In [55], the authors suggested a histogram of states-based strategy for learning compact and discriminative embeddings for dysarthric voice detection using the deep neural networkhidden Markov model. Srinivasan et al [56] proposed a multi-view representation-based disordered speech recognition system based on auditory image-based features and cepstral characteristics, showing improved performance in recognizing very low intelligibility words compared to conventional methods.…”

Section: Assessing Speech-signal Impairmentsmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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A hybrid approach to detecting Parkinson's disease using spectrogram and deep learning CNN-LSTM network

Shibina,

Thasleema

2024

Int J Speech Technol

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Histogram of States Based Assistive System for Speech Impairment Due to Neurological Disorders

Cited by 8 publications

References 43 publications

State-of-the-Art Analysis of Deep Learning-Based Monaural Speech Source Separation Techniques

State-of-the-Art Analysis of Deep Learning-Based Monaural Speech Source Separation Techniques

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

A hybrid approach to detecting Parkinson's disease using spectrogram and deep learning CNN-LSTM network

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