Deep representation learning in speech processing: Challenges, recent advances, and future trends
Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …
engineered acoustic features (feature engineering) as a separate distinct problem from the …
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
Trends in speech emotion recognition: a comprehensive survey
K Kaur, P Singh - Multimedia Tools and Applications, 2023 - Springer
Among the other modes of communication, such as text, body language, facial expressions,
and so on, human beings employ speech as the most common. It contains a great deal of …
and so on, human beings employ speech as the most common. It contains a great deal of …
Audio-visual emotion recognition in video clips
This paper presents a multimodal emotion recognition system, which is based on the
analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral …
analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral …
MFF-SAug: Multi feature fusion with spectrogram augmentation of speech emotion recognition using convolution neural network
S Jothimani, K Premalatha - Chaos, Solitons & Fractals, 2022 - Elsevier
Abstract The Speech Emotion Recognition (SER) is a complex task because of the feature
selections that reflect the emotion from the human speech. The SER plays a vital role and is …
selections that reflect the emotion from the human speech. The SER plays a vital role and is …
Learning deep multimodal affective features for spontaneous speech emotion recognition
S Zhang, X Tao, Y Chuang, X Zhao - Speech Communication, 2021 - Elsevier
Recently, spontaneous speech emotion recognition has become an active and challenging
research subject. This paper proposes a new method of spontaneous speech emotion …
research subject. This paper proposes a new method of spontaneous speech emotion …
Semisupervised autoencoders for speech emotion recognition
Despite the widespread use of supervised learning methods for speech emotion recognition,
they are severely restricted due to the lack of sufficient amount of labelled speech data for …
they are severely restricted due to the lack of sufficient amount of labelled speech data for …
Hybrid deep learning with optimal feature selection for speech emotion recognition using improved meta-heuristic algorithm
K Manohar, E Logashanmugam - Knowledge-based systems, 2022 - Elsevier
Speech emotion recognition is the crucial stream in emotional computing and also create
few issues owing to its complication in processing. The efficiency of the acoustic methods …
few issues owing to its complication in processing. The efficiency of the acoustic methods …
Spontaneous speech emotion recognition using multiscale deep convolutional LSTM
Recently, emotion recognition in real sceneries such as in the wild has attracted extensive
attention in affective computing, because existing spontaneous emotions in real sceneries …
attention in affective computing, because existing spontaneous emotions in real sceneries …
Semi-supervised speech emotion recognition with ladder networks
S Parthasarathy, C Busso - IEEE/ACM transactions on audio …, 2020 - ieeexplore.ieee.org
Speech emotion recognition (SER) systems find applications in various fields such as
healthcare, education, and security and defense. A major drawback of these systems is their …
healthcare, education, and security and defense. A major drawback of these systems is their …