Study of feature combination using HMM and SVM for multilingual Odiya speech emotion recognition

Swain, Monorama; Sahoo, Subhasmita; Routray, Aurobinda; Kabisatpathy, Prithviraj; Kundu, Jogendra Nath

doi:10.1007/s10772-015-9275-7

Cited by 20 publications

(5 citation statements)

References 15 publications

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“…For crossvalidation, the experiment is repeated n times with a different test speaker each time. In the second method, the training and testing sets have been determined previously [17,49,50].…”

Section: Methodsmentioning

confidence: 99%

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

Retta,

Sutcliffe,

Mahmood

et al. 2023

Applied Sciences

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In a conventional speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language do not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German, and Urdu. For Amharic, we use our own publicly available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu, we use the existing RAVDESS, EMO-DB, and URDU datasets. We followed previous research in mapping labels for all of the datasets to just two classes: positive and negative. Thus, we can compare performance on different languages directly and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. The results, averaged for the three models, were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each of the following pairs: Amharic↔German, Amharic↔English, and Amharic↔Urdu. The results with Amharic as the target suggested that using English or German as the source gives the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percentage points greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training an SER classifier when resources for a language are scarce.

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Section: Methodsmentioning

confidence: 99%

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

Retta,

Sutcliffe,

Mahmood

et al. 2023

Applied Sciences

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“…In this paper, log likelihood of each feature vector that represents a given action is computed and the expectation maximization method is used to maximize the log likelihood estimation (Swain et al , 2015). In particular, our method can be effective when sequential observations of different actions have variable-lengths, which will be explained in the following.…”

Section: Proposed Model and Methodologymentioning

confidence: 99%

Affective actions recognition in dyadic interactions based on generative and discriminative models

Yang

Wang

Zhao

et al. 2020

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Purpose Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions. Design/methodology/approach A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation). Findings Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN. Practical implications The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities. Originality/value The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

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“…Various features like MFCC, log power, delta MFCC, double delta MFCC, LPCC, and LFPC have been used with HMM and SVM to classify seven different emotions [76]. MFCC obtained the best accuracy of 82.14% for SVM and performed consistently with less computational complexity.…”

Section: Review Of Speech Emotion Recognitionmentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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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.

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Study of feature combination using HMM and SVM for multilingual Odiya speech emotion recognition

Cited by 20 publications

References 15 publications

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

Affective actions recognition in dyadic interactions based on generative and discriminative models

A Comprehensive Review of Speech Emotion Recognition Systems

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