• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.614-620
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• 2019

In this paper, we propose a method to effectively determine the representative style embedding of each emotion class to improve the global style token-based end-to-end speech synthesis system. The emotion expressiveness of conventional approach was limited because it utilized only one style representative per each emotion. We overcome the problem by extracting multiple number of representatives per each emotion using a k-means clustering algorithm. Through the results of listening tests, it is proved that the proposed method clearly express each emotion while distinguishing one emotion from others.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1137-1142
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• 2010

This paper studied the speech parameters less affected by the human emotion for the development of the robust speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient and frequency warped mel-cepstral coefficient were used as feature parameters. And CMS (Cepstral Mean Subtraction) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using vocal tract length normalized mel-cepstral coefficient, its derivatives and CMS as a signal bias removal showed the best performance of 0.78% word error rate. This corresponds to about a 50% word error reduction as compare to the performance of baseline system using mel-cepstral coefficient, its derivatives and CMS.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.655-660
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• 2005

This paper studied the feature parameters less affected by the emotional variation for the development of the robust speech recognition technologies. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. In this study, LPC cepstral coefficient, met-cepstral coefficient, root-cepstral coefficient, PLP coefficient, RASTA met-cepstral coefficient were used as a feature parameters. And CMS and SBR method were used as a signal bias removal techniques. Experimental results showed that the HMM based speaker independent word recognizer using RASTA met-cepstral coefficient :md its derivatives and CMS as a signal bias removal showed the best performance of $7.05\%$ word error rate. This corresponds to about a $52\%$ word error reduction as compare to the performance of baseline system using met - cepstral coefficient.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.366-369
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• 2000

This paper examines three algorithms to recognize speaker's emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likeligood Bayes), NN(Nearest Neighbor) and HMM (Hidden Markov Model) algorithms are used as the pattern matching techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB and NN are composed of pitch mean, pitch standard deviation, energy mean, energy standard deviation, etc. For HMM, vectors of delta pitch with delta-delta pitch and delta energy with delta-delta energy are used. We recorded a corpus of emotional speech data and performed the subjective evaluation for the data. The subjective recognition result was 56% and was compared with the classifiers' recognition rates. MLB, NN, and HMM classifiers achieved recognition rates of 68.9%, 69.3% and 89.1% respectively, for the speaker dependent, and context-independent classification.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.123-125
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• 2004

Recently, we are getting to interest in a user friendly machine. The emotion is one of most important conditions to be familiar with people. The machine uses sound or image to express or recognize the emotion. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.81.2-81
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• 2002

$\textbullet$ Introduction- Concept and meaning of the emotional Recognition $\textbullet$ The feature of 4-emotions $\textbullet$ Pitch(approach) $\textbullet$ Simulator-structure, RNN(learning algorithm), evaluation function, solution search method $\textbullet$ Result

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.477-482
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• 2009

The emotion recognition system in commercial environments such as call-center undergoes severe system performance degradation and instability due to the speech characteristic differences between the system training database and the input speech of unspecified customers. In order to alleviate these problems, this paper extends traditional method of emotion recognition of neutral/anger into two-step hierarchical structure by using emotional characteristic changes and differences of male and female. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance about 25% over the traditional method of emotion recognition.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.15-18
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• 2002

Prosodic features of speech are known to play an important role in the transmission of linguistic information in human conversation. Their roles in the transmission of para- and non- linguistic information are even much more. In spite of their importance in human conversation, from engineering viewpoint, research focuses are mainly placed on segmental features, and not so much on prosodic features. With the aim of promoting research works on prosody, a research project 'Prosody and Speech Processing' is now going on. A rough sketch of the project is first given in the paper. Then, the paper introduces several prosody-related research works, which are going on in our laboratory. They include, corpus-based fundamental frequency contour generation, speech rate control for dialogue-like speech synthesis, analysis of prosodic features of emotional speech, reply speech generation in spoken dialogue systems, and language modeling with prosodic boundaries.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.45-50
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• 2011

Formant structure of speech associated with five different emotions (anger, fear, happiness, neutral, sadness) was analysed. Acoustic separability of vowels (or emotions) associated with a specific emotion (or vowel) was estimated using F-ratio. According to the results, neutral showed the highest separability of vowels followed by anger, happiness, fear, and sadness in descending order. Vowel /A/ showed the highest separability of emotions followed by /U/, /O/, /I/ and /E/ in descending order. The acoustic results were interpreted and explained in the context of previous articulatory and perceptual studies. Suggestions for the performance improvement of an automatic emotion recognition system and automatic speech recognition system were made.