An approach for speech enhancement with dysarthric speech recognition using optimization based machine learning frameworks

Jolad, Bhuvaneshwari; Khanai, Rajashri

doi:10.1007/s10772-023-10019-y

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…In Jolad, B and Khanai, R [15], speech signal quality can be improved by using a fractional competitive crowd search algorithm (FCCSA). When the suggested technique is evaluated using the UA speech database, it yields 0.930, 0.933, and 0.934 in terms of accuracy, specificity, and sensitivity.…”

Section: Literature Surveymentioning

confidence: 99%

“…Each elephant m x denotes the new position is influenced by the matriarch m x . The clan m_x elephant 'y' can be determined using (15) where [0,1] is a scaling factor, P best, m x is the location with the best fitness value inside clan "x", and P n,m x,y represent the old and new positions of elephant "y" in clan x, respectively. With a normal distribution and a value between [0, 1], L is a random number.…”

Section: Fig 3 Flow Diagram Of the Proposed Enhance Ehomentioning

confidence: 99%

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Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Pathrose,

Ismail M,

Mohan P

2023

Int. j. electr. comput. eng. syst. (Online)

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In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments.

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Section: Literature Surveymentioning

confidence: 99%

Section: Fig 3 Flow Diagram Of the Proposed Enhance Ehomentioning

confidence: 99%

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Pathrose,

Ismail M,

Mohan P

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Automatic speech recognition (ASR) aims to develop technology capable of comprehending and reacting to human speech (Tan and Wang, 2021). It deals with processing written and spoken words as a subfield of natural language processing and voice processing (Jolad and Khanai, 2023). To enable the automated transcription of spoken words into text, it uses machine learning algorithms to assess and understand the spoken language.…”

Section: Introductionmentioning

confidence: 99%

A Speech Disorder Detection Model Using Ensemble Learning Approach

Dutta,

Wahab Sait

2024

Journal of Disability Research

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Speech disorder detection (SDD) models can assist speech therapists in providing personalized treatment to individuals with speech impairment. Speech disorders (SDs) comprise a broad spectrum of problems that affect the production, articulation, fluency, and quality of speech. Prompt identification and timely intervention are essential for efficient control and therapy of speech problems. However, current diagnostic techniques often depend on subjective evaluations by speech-language pathologists and may encounter challenges in terms of accessibility, scalability, and consistency. The limited dataset and substantial computational power reduce the efforts for developing automated SDD models. However, recent technology developments enable researchers to determine key factors to classify voice samples. The voice sample classification can identify the severity of the SD. Ensemble learning (EL) facilitates the integration of multiple models’ predictions to generate an optimal outcome. Hence, an EL-based SDD model is introduced in this study. A mel-spectrogram (MS) generator is built to produce images using the voice samples. The authors suggested a feature engineering based on the ResNet 18 model for extracting crucial features from the MS. CatBoost and XGBoost models are employed to classify the features. The outcomes of these models are used to train the support vector machine model to make a final prediction. The VOice ICarfEDerico II (VOICED) dataset is used to generalize the proposed model. The experimental findings indicate that the recommended SDD model outperforms the state-of-the-art models by yielding an outstanding performance. This suggests that the model can assist speech therapists in offering customized speech therapies based on the SD severity. The model performance can be improved using multilanguage voice samples.

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