AIMC Topic: Speech

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ConAnomaly: Content-Based Anomaly Detection for System Logs.

Sensors (Basel, Switzerland)
Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log pa...

Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human-Robot Conversations.

Sensors (Basel, Switzerland)
Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43)
Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of criticism expressed by the caregiver to the care recipient, with a high level of criticism a ...

Multi-Modal Residual Perceptron Network for Audio-Video Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion recognition is an important research field for human-computer interaction. Audio-video emotion recognition is now attacked with deep neural network modeling tools. In published papers, as a rule, the authors show only cases of the superiority...

Sch-net: a deep learning architecture for automatic detection of schizophrenia.

Biomedical engineering online
BACKGROUND: Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. Clinically, schizophrenia with negative symptoms is usually misdiagnosed. The diagnosis is also dependent on the experienc...

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation.

Sensors (Basel, Switzerland)
We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data...

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...

Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance...

Speech signal enhancement in cocktail party scenarios by deep learning based virtual sensing of head-mounted microphones.

Hearing research
The cocktail party effect refers to the human sense of hearing's ability to pay attention to a single conversation while filtering out all other background noise. To mimic this human hearing ability for people with hearing loss, scientists integrate ...

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Brain and language
Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language diso...