ArticleSensors (Basel, Switzerland)2021
On the Speech Properties and Feature Extraction Methods in Speech Emotion Recognition.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.Applied psychophysiology and biofeedback · 2024Trial
- Feature selection for emotion recognition in speech: a comparative study of filter and wrapper methods.PeerJ. Computer science · 2025Article
- End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model.Sensors (Basel, Switzerland) · 2022Article
- Speech Emotion Recognition Based on Modified ReliefF.Sensors (Basel, Switzerland) · 2022Article
- Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Frequency, Time, Representation and Modeling Aspects for Major Speech and Audio Processing Applications.Sensors (Basel, Switzerland) · 2022Article
- The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.Computational intelligence and neuroscience · 2022Article
- Global and local feature fusionFrontiers in neurorobotics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Many speech emotion recognition systems have been designed using different features and classification methods. Still, there is a lack of knowledge and reasoning regarding the underlying speech characteristics and processing, i.e., how basic characteristics, methods, and settings affect the accuracy, to what extent, etc. This study is to extend physical perspective on speech emotion recognition by analyzing basic speech characteristics and modeling methods, e.g., time characteristics (segmentation, window types, and classification regions-lengths and overlaps), frequency ranges, frequency scales, processing of whole speech (spectrograms), vocal tract (filter banks, linear prediction coefficient (LPC) modeling), and excitation (inverse LPC filtering) signals, magnitude and phase manipulations, cepstral features, etc. In the evaluation phase the state-of-the-art classification method and rigorous statistical tests were applied, namely N-fold cross validation, paired
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.