Recognizing emotions from human speech

Khanna, Preeti; Sasikumar, M.

doi:10.1007/978-81-8489-989-4_40

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…Owkwon made analysis on emotional expressions using pitch, log-energy, mel bond energies, formant, MFCC's (base features) and Gaussian mixture model, SVM in 2003. The accuracy was about 96.3% [3]. In the same year Gobl and Chasaide said that voice quality is reasonable for certain emotions.…”

Section: Literature Surveymentioning

confidence: 94%

“…In 1993, Murray and Arnott [2] done an analysis on qualitative correlation between emotion and speech features such as pitch, intensity and timing of utterances. During the period of 1998 to 1999, Petrushin distinguished between agitation (Anger, Happy, Fear) and calm (Sad, Neutral) a type of emotions using RELIEF-F algorithm [3], K-NN, ANN classifiers obtained 43 features such as min, max, range, sd etc., but selected only top 14 features and the accuracy level was about 77% for normal state and sadness state. During the period of 2001, Nwe made analysis taking six emotions exhibited by two speakers with 12 MFCC features as input to discrete Hidden Markov Model (HMM) and the accuracy was 70%.…”

Section: Literature Surveymentioning

confidence: 99%

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Speech Emotion Recognition: A Survey

Kuchibhotla¹

2019

IJMUE

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Speech Recognition is a tremendous application from the history that is identification and conversion of spoken words into text. The performance and quality of work had increased a lot. This performance lead to the research work on Emotion Recognition based on the language spoken that is obtaining the kind of emotion from the spoken speech which is an application based on human-robot interactions. Emotions can be recognized in a better way using Speech processing, Artificial Intelligence techniques and linguistic semantics. Systems are given training in such a way to detect the emotions from the spoken utterances. This paper contains about the survey from the history to the present works that took place in the speech emotion recognition and also the experiment results. The survey contains about the works that took place from the by different scientists and their usage of different features, classifiers etc. The paper also holds three categories, one is different databases that are involved, second is what features are involved for representation of speech and third about the classification schemes. The survey also includes the conclusions of performances and limitations of current speech emotion recognition.

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Section: Literature Surveymentioning

confidence: 94%

Section: Literature Surveymentioning

confidence: 99%

Speech Emotion Recognition: A Survey

Kuchibhotla¹

2019

IJMUE

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“…This paper explores how to combine the information from various sources (e.g. facial expression [24], speech [21,22,23] and others [20]) to achieve better recognition of emotional state using rule based approach.…”

Section: The Problem Domain For Multimodal Emotion Recognitionmentioning

confidence: 99%

“…We will use facial expressions as the running example to illustrate these stages, etc. The framework remains same across all modalities [20,21,22].…”

Section: Framework For Emotion Recognitionmentioning

confidence: 99%

Rule Based System for Recognizing Emotions Using Multimodal Approach

Khanna¹,

Sasikumar²

2013

IJACSA

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Abstract-Emotion is assuming increasing importance in human computer interaction (HCI), in general, with the growing feeling that emotion is central to human communication and intelligence. Users expect not just functionality as a factor of usability, but experiences, matched to their expectations, emotional states, and interaction goals. Endowing computers with this kind of intelligence for HCI is a complex task. It becomes more complex with the fact that the interaction of humans with their environment (including other humans) is naturally multimodal. In reality, one uses a combination of modalities and they are not treated independently. In an attempt to render HCI more similar to human-human communication and enhance its naturalness, research on multiple modalities of human expressions has seen ongoing progress in the past few years. As compared to unimodal approaches, various problems arise in case of multimodal emotion recognition especially concerning fusion architecture of multimodal information. In this paper we will be proposing a rule based hybrid approach to combine the information from various sources for recognizing the target emotions. The results presented in this paper shows that it is feasible to recognize human affective states with a reasonable accuracy by combining the modalities together using rule based system.

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“…Currently, speech emotion recognition (SER) is a growing research area that aims to recognize the emotional state of a speaker from the speech signal. It has potential applications both for the study of human-human communication and human-computer interaction (HCI) [1].…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition from Social Media Voice Messages Recorded in the Wild

Gómez-Zaragozá

Marín‐Morales

Parra

et al. 2020

Communications in Computer and Information Science

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Speech is the most natural way for human communication, carrying the emotional state of the speaker that plays an important role in social interaction. Currently, many instant messaging apps offer the possibility of exchanging voice audios with other users. As a result, a great amount of voice data is generated every day, representing a new challenging approach for speech emotion recognition in real environments. In this study, we investigated emotion recognition from voice messages recorded in the wild using machine-learning algorithms. Unlike most research in this field, which use databases based on emotions evoked in lab environments, simulated by actors or subjectively selected from radio or TV talks, we created an ecological speech dataset with audios from real WhatsApp conversations of 30 Spanish speakers. Four external evaluators labelled each audio in terms of arousal and valence using the Self-Assessment Manikin (SAM) procedure. Pre-processing techniques were applied to the audios and different time and frequency domain features were extracted. Supervised machine learning classifiers were computed using feature reduction and hyper-parameter tuning in order to recognize the affective state of each voice message. The best recognition rate was obtained with Support Vector Machines, achieving 71.37% along the arousal dimension and 70.73% along the valence dimension. These results support the use of emotion recognition models on daily communication apps, helping to understand social human behavior and their interactions with devices in the real world.

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Recognizing emotions from human speech

Cited by 7 publications

References 5 publications

Speech Emotion Recognition: A Survey

Speech Emotion Recognition: A Survey

Rule Based System for Recognizing Emotions Using Multimodal Approach

Speech Emotion Recognition from Social Media Voice Messages Recorded in the Wild

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