An Audio Processing Approach using Ensemble Learning for Speech-Emotion Recognition for Children with ASD

Valles, Damian; Matin, Rezwan

doi:10.1109/aiiot52608.2021.9454174

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…The algorithm's decision-making process employs the technique for creating data files for each sample file. The first file to load is the speech.mat example file [25]. After the file has been loaded, the file's name, categories, and emotions are extracted by path.…”

Section: Fig 2 Description Of Speech Samples For Training and Validationmentioning

confidence: 99%

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

Siddiqui,

Haider,

Nazir

et al. 2024

Mehran Univ. res. j. eng. technol.

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Since the last decade, autism spectrum disorder (ASD) has been used as a general term to describe a wide range of conditions, including autistic syndrome, Asperger's disorder, and pervasive developmental disability. This problem emerges as a decreased ability to share emotions and a greater difficulty understanding others' feelings, leading to increased social communication difficulties. To assist patients with ASD, we proposed a concept that incorporates speech emotion detection technologies, which are widely used in the field of human-computer interaction (particularly youngsters). An algorithm based on a novel method for classifying normal and autistic children's speech emotions is implemented in this article. The training data set is treated to a new approach after all features have been extracted. The technique discussed in this study is the creation of a hybrid algorithm that serves as a classifier for normal and autistic children's speech emotions. Voice emotion recognition can be identified accurately and with a lower error rate. The data collection includes speech samples from 200 normal and 250 autistic groups in four moods (Angry, Happy, Neutral and Sad). As per research findings, the implemented hybrid algorithm for Normal and Autistic Children Speech Emotions (SERNAC) outperformed the existing classifiers by increasing accuracy.

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Section: Fig 2 Description Of Speech Samples For Training and Validationmentioning

confidence: 99%

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

Siddiqui,

Haider,

Nazir

et al. 2024

Mehran Univ. res. j. eng. technol.

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“…Ensemble learning (EL) assists in recognizing SDs by addressing generalization, robustness, and speech data diversity (Valles and Matin, 2021). Combining numerous models improves the performance and reliability of the detection system, making it a valuable method for constructing reliable speech disorder detection (SDD) models.…”

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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A Distributed Ensemble Machine Learning Technique for Emotion Classification from Vocal Cues

Vijayan¹,

Soman²,

Vivek³

et al. 2022

Lecture Notes in Computer Science

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An Audio Processing Approach using Ensemble Learning for Speech-Emotion Recognition for Children with ASD

Cited by 12 publications

References 14 publications

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

A Speech Disorder Detection Model Using Ensemble Learning Approach

A Distributed Ensemble Machine Learning Technique for Emotion Classification from Vocal Cues

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