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Speech

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Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent s...

Encoding of speech in convolutional layers and the brain stem based on language experience.

Scientific reports
Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computat...

An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse.

Neural networks : the official journal of the International Neural Network Society
While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. A...

Evidence of a predictive coding hierarchy in the human brain listening to speech.

Nature human behaviour
Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of...

Robot leadership-Investigating human perceptions and reactions towards social robots showing leadership behaviors.

PloS one
Human-robot interaction research has shown that social robots can interact with humans in complex social situations and display leadership-related behaviors. Therefore, social robots could be able to take on leadership roles. The aim of our study was...

Multimodal Sensor-Input Architecture with Deep Learning for Audio-Visual Speech Recognition in Wild.

Sensors (Basel, Switzerland)
This paper investigates multimodal sensor architectures with deep learning for audio-visual speech recognition, focusing on in-the-wild scenarios. The term "in the wild" is used to describe AVSR for unconstrained natural-language audio streams and vi...

Human-Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention.

Sensors (Basel, Switzerland)
Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker's emotional state from their speech signal. SER is ...

A Survey on Low-Latency DNN-Based Speech Enhancement.

Sensors (Basel, Switzerland)
This paper presents recent advances in low-latency, single-channel, deep neural network-based speech enhancement systems. The sources of latency and their acceptable values in different applications are described. This is followed by an analysis of t...

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition.

Sensors (Basel, Switzerland)
Speech reflects people's mental state and using a microphone sensor is a potential method for human-computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affect...

Qualitative and Artificial Intelligence-based Sentiment Analyses of Anti-LGBTI+ Hate Speech on Twitter in Turkey.

Issues in mental health nursing
The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were ev...