Detection of stress and emotion in speech using traditional and FFT based log energy features

Nwe, Tin Lay; Foo, Say Wei; Silva, Liyanage C. De

doi:10.1109/icics.2003.1292741

Cited by 38 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Short-term power spectrum is presented by Mel-frequency cepstral coefficients, whereas vocal tract characteristics are presented by linear prediction coefficients. Logarithmic filtering of auditory system is characterized by log-frequency power coefficients using Fourier transform [7]. Voice quality measurements, such as jitter, harmonics-tonoise ratio, and shimmer, exploit the relation between vocal tract characteristics and emotion content.…”

Section: Introductionmentioning

confidence: 99%

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Emotion recognition system from speech signal is a widely researched topic in the design of the Human–Computer Interface (HCI) models, since it provides insights into the mental states of human beings. Often, it is required to identify the emotional condition of the humans as cognitive feedback in the HCI. In this paper, an attempt to recognize seven emotional states from speech signals, known as sad, angry, disgust, happy, surprise, pleasant, and neutral sentiment, is investigated. The proposed method employs a non-linear signal quantifying method based on randomness measure, known as the entropy feature, for the detection of emotions. Initially, the speech signals are decomposed into Intrinsic Mode Function (IMF), where the IMF signals are divided into dominant frequency bands such as the high frequency, mid-frequency , and base frequency. The entropy measures are computed directly from the high-frequency band in the IMF domain. However, for the mid- and base-band frequencies, the IMFs are averaged and their entropy measures are computed. A feature vector is formed from the computed entropy measures incorporating the randomness feature for all the emotional signals. Then, the feature vector is used to train a few state-of-the-art classifiers, such as Linear Discriminant Analysis (LDA), Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Gradient Boosting Machine. A tenfold cross-validation, performed on a publicly available Toronto Emotional Speech dataset, illustrates that the LDA classifier presents a peak balanced accuracy of 93.3%, F1 score of 87.9%, and an area under the curve value of 0.995 in the recognition of emotions from speech signals of native English speakers.

show abstract

Section: Introductionmentioning

confidence: 99%

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…In [51], the authors demonstrated that HMM performs better on log frequency power coefficient features than LPCC and MFCC. The emotion classification was done based on text-independent methods.…”

Section: ) Hidden Markov Model (Hmm)mentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

View full text Add to dashboard Cite

During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

show abstract

“…Studies have shown that the low-level audio features of pitch, Zero-Crossing Rate (ZCR), Log-Energy (LE), Teager Energy Operator (TEO), and Mel-Frequency Cepstral Coefficients (MFCC) can determine the emotional state of music audio signals [60][61][62][63]. The extraction methods for these features are illustrated as follows:…”

Section: Low-level Audio Features Extractionmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

View full text Add to dashboard Cite

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

show abstract

Detection of stress and emotion in speech using traditional and FFT based log energy features

Cited by 38 publications

References 11 publications

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

A Comprehensive Review of Speech Emotion Recognition Systems

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Contact Info

Product

Resources

About