AIMC Topic: Speech

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Neurogenerative Disease Diagnosis in Cepstral Domain Using MFCC with Deep Learning.

Computational and mathematical methods in medicine
Because underlying cognitive and neuromuscular activities regulate speech signals, biomarkers in the human voice can provide insight into neurological illnesses. Multiple motor and nonmotor aspects of neurologic voice disorders arise from an underlyi...

Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.

Computational intelligence and neuroscience
Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they ar...

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been...

Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier.

Journal of healthcare engineering
Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information...

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm.

Computational intelligence and neuroscience
As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based a...

The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning.

Sensors (Basel, Switzerland)
Machine Learning (ML) algorithms within a human-computer framework are the leading force in speech emotion recognition (SER). However, few studies explore cross-corpora aspects of SER; this work aims to explore the feasibility and characteristics of ...

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.

Sensors (Basel, Switzerland)
Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still cha...

Decoding lip language using triboelectric sensors with deep learning.

Nature communications
Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we pro...

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.

Frontiers in public health
Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and s...

Detection of Emotion of Speech for RAVDESS Audio Using Hybrid Convolution Neural Network.

Journal of healthcare engineering
Every human being has emotion for every item related to them. For every customer, their emotion can help the customer representative to understand their requirement. So, speech emotion recognition plays an important role in the interaction between hu...