AIMC Topic: Speech

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Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning.

PloS one
How can we build accurate transcription models for both ordinary speech and characterized speech in a semi-supervised setting? ASR (Automatic Speech Recognition) systems are widely used in various real-world applications, including translation system...

Hierarchical dynamic coding coordinates speech comprehension in the human brain.

Proceedings of the National Academy of Sciences of the United States of America
Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about h...

Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.

Journal of medical Internet research
BACKGROUND: Automated speech and language analysis (ASLA) is gaining momentum as a noninvasive, affordable, and scalable approach for the early detection of Alzheimer disease (AD). Nevertheless, the literature presents 2 notable limitations. First, m...

Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications.

JMIR mental health
BACKGROUND: The field of speech emotion recognition (SER) encompasses a wide variety of approaches, with artificial intelligence technologies providing improvements in recent years. In the domain of mental health, the links between individuals' emoti...

A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments.

Scientific data
The global surge in depression rates, notably severe in China with over 95 million affected, underscores a dire public health issue. This is exacerbated by a critical shortfall in mental health professionals, highlighting an urgent call for innovativ...

Wordsworth: A generative word dataset for comparison of speech representations in humans and neural networks.

Scientific data
Speech perception is fundamental for human communication, but its neural basis is not well understood. Furthermore, while modern neural networks (NNs) can accurately recognize speech, whether they effectively model human speech processing remains unc...

Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.

Biomedical physics & engineering express
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to ...

Searching for effective preprocessing method and CNN based architecture with efficient channel attention on speech emotion recognition.

Scientific reports
Recently, Speech emotion recognition (SER) performance has steadily increased as multiple deep learning architectures have adapted. Especially, convolutional neural network (CNN) models with spectrogram data preprocessing are the most popular approac...

Auto-Masked Audio Spectrogram Transformer for depression detection from speech.

Journal of affective disorders
BACKGROUND: Depression is a psychological disorder characterized by altered self-referential cognition and impaired emotional expression. Traditional diagnostic methods can be costly or intrusive, while Speech-based analysis offers an accessible alte...

Syllable-based speech characteristics as potential biomarker for differential diagnosis of Parkinson's disease, multiple system atrophy, and cerebellar ataxia.

Journal of neurology
Speech disorders differ between Parkinson's disease (PD) and multiple system atrophy (MSA), but studies focusing on group differences based on syllables or including cerebellar ataxia (CA) are lacking until now. This cross-sectional study aimed to an...