Investigation of DNN-HMM and Lattice Free Maximum Mutual Information Approaches for Impaired Speech Recognition

Veni, S. Vishnika; Chandrakala, S.

doi:10.1109/access.2021.3129847

Cited by 7 publications

(1 citation statement)

References 34 publications

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“…Gupta et al [52] suggested a residual network-based approach for detecting dysarthria severity level based on short speech segments, whereas Latha et al [53] employed deep learning and several acoustic cues to recognize dysarthric speech and generate discernible speech. 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.…”

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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