Towards Emotion Recognition from Speech: Definition, Problems and the Materials of Research

Anagnostopoulos, Christos-Nikolaos; Iliou, Theodoros

doi:10.1007/978-3-642-11684-1_8

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Linguists plays a major role to invent emotional states that mostly encountered in our lives. Linguistic information through words detect emotions in qualitative patterns [12]. But the drawback of linguistic information is cross culture diversities.…”

Section: Figure 1 Process Involved In Speech Emotion Recognition Systemmentioning

confidence: 99%

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: Figure 1 Process Involved In Speech Emotion Recognition Systemmentioning

confidence: 99%

Speech Emotion Recognition: A Survey

kuchibhotla¹

2019

IJMUE

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“…After processing with PRAAT software 51 each utterance was converted to a 133-dimensional prosodic feature vector based on well-established speech features, such as Pitch, Mel Frequency Cepstral Coefficients (MFCCs), energy and formant frequencies. 52 Thus, the dataset consists of 535 samples with 133 prosodic input variables to be categorized in 7 class labels. The second dataset (ECO) comprises of 188 seawater samples collected on monthly campaigns during one annual cycle (August '04-July '05) in Kalloni Gulf, Lesvos Island, Greece.…”

Section: Datasetsmentioning

confidence: 99%

Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms

Tamvakis

Anagnostopoulos

Tsirtsis

et al. 2018

Int. J. Artif. Intell. Tools

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Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search.

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“…This module extracts the feature vector from the speech. We employed popular features, two spectral features (MFCC: Mel frequency cepstral coefficient, LPC: Linear predictive coding) and two prosodic features (pitch, energy), in existing methods of the speech emotion recognition area [ 31 , 32 ]. The reasons for selecting these features are shown in Table 1 .…”

Section: Robust Personalized Emotion Recognition Frameworkmentioning

confidence: 99%

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Bang

Hur

Kim

et al. 2018

Sensors

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Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

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Towards Emotion Recognition from Speech: Definition, Problems and the Materials of Research

Cited by 15 publications

References 18 publications

Speech Emotion Recognition: A Survey

Speech Emotion Recognition: A Survey

Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

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