• Phonetics and Speech Sciences
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• v.9 no.3
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• pp.41-47
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• 2017

The purpose of this study is to examine how speakers use VOT and f0 to distinguish tense, lax, and aspirated stops in isolated sentence reading and paragraph readings. To do so, a total of 20 males between the ages of 20-25 years old were asked to read (1) isolated sentences, (2) information-oriented text and (3) emotional expressive texts in which the stop pronunciation's VOT value and f0 were measured thereafter. The main results are as follows. In the isolate sentence reading, lax stops, and aspirated stops were distinguished by both VOT and f0, but for the Korean men that read reading texts, VOT is not a cue to distinguish between lax and aspirated stops. In general, the VOT differences between lax stops and aspirated stops were smaller for information-oriented texts and emotional expressive texts than that of the isolate sentence reading. In the paragraph reading that induces a natural utterance, the f0 dependence is greater for the distinction between lax and aspirated stops.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.67-76
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• 2015

The purpose of this study was to compare professional (Pro) and non-professional (Non-pro) voice users with voice disorders in self-reporting voice evaluation using Korean-Voice Handicap Index (K-VHI) and Korean-Voice Related Quality of Life (K-VRQOL). In addition, those were compared by voice quality and voice disorder type. 94 Pro and 106 Non-pro were asked to fill out the K-VHI and K-VRQOL, perceptually evaluated on GRBAS scales, and divided into three types of voice disorders (functional, organic and neurologic) by an experienced speech-language pathologist and an otolaryngologist. The results showed that the functional (F) and physical (P) scores of K-VHI in Pro group were significantly higher than those in Non-pro group. As the voice quality evaluated by G scale got worse, the scores of all aspects except emotional (E) of K-VHI and social-emotional (SE) of K-VRQOL were higher. All scores of K-VHI and K-VRQOL in neurologic voice disorders were significantly higher than those in functional and organic voice disorders. In conclusion, professional voice users are more sensitive to their functional and physical handicap resulted by their voice problems and that goes double for the patients with severe and neurologic voice disorders.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.453-455
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• 2020

본 논문은 시각 장애인을 위한 감정 음성 자막 서비스를 생성하는 종단 간(end-to-end) 감정 음성 합성 시스템(emotional text-to-speech synthesis system, TTS)의 음성 합성 속도를 높이면서도 합성음의 음질을 향상시키는 방법을 제안한다. 기존에 사용했던 전역 스타일 토큰(Global Style Token, GST)을 이용한 감정 음성 합성 방법은 다양한 감정을 표현할 수 있는 장점을 갖고 있으나, 합성음을 생성하는데 필요한 시간이 길고 학습할 데이터의 동적 영역을 효과적으로 처리하지 않으면 합성음에 클리핑(clipping) 현상이 발생하는 등 음질이 저하되는 양상을 보였다. 이를 보안하기 위해 본 논문에서는 새로운 데이터 전처리 과정을 도입하였고 기존의 보코더(vocoder)인 웨이브넷(WaveNet)을 웨이브알엔엔(WaveRNN)으로 대체하여 생성 속도와 음질 측면에서 개선됨을 보였다.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.21-26
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• 2002

This paper presents the new approach of identifying human emotional states such as anger, happiness, normal, sadness, or surprise. This is accomplished by using discrete duration continuous hidden Markov models(DDCHMM). For this, the emotional feature parameters are first defined from input speech signals. In this study, we used prosodic parameters such as pitch signals, energy, and their each derivative, which were then trained by HMM for recognition. Speaker adapted emotional models based on maximum a posteriori(MAP) estimation were also considered for speaker adaptation. As results, the simulation performance showed that the recognition rates of vocal emotion gradually increased with an increase of adaptation sample number.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1777-1778
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• 2007

In this paper, We design and implement a humanoid robot, With Educational purpose, which can collaborate and communicate with human. We present an affective human-robot communication system for a humanoid robot, D2, which we designed to communicate with a human through dialogue. D2 communicates with humans by understanding and expressing emotion using facial expressions, voice, gestures and posture. Interaction between a human and a robot is made possible through our affective communication framework. The framework enables a robot to catch the emotional status of the user and to respond appropriately. As a result, the robot can engage in a natural dialogue with a human. According to the aim to be interacted with a human for voice, gestures and posture, the developed Educational humanoid robot consists of upper body, two arms, wheeled mobile platform and control hardware including vision and speech capability and various control boards such as motion control boards, signal processing board proceeding several types of sensors. Using the Educational humanoid robot D2, we have presented the successful demonstrations which consist of manipulation task with two arms, tracking objects using the vision system, and communication with human by the emotional interface, the synthesized speeches, and the recognition of speech commands.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.255-258
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• 2011

An application of Gammatone auditory filterbank for emotion recognition from speech is described in this paper. Gammatone filterbank is a bank of Gammatone filters which are used as a preprocessing stage before applying feature extraction methods to get the most relevant features for emotion recognition from speech. In the feature extraction step, the energy value of output signal of each filter is computed and combined with other of all filters to produce a feature vector for the learning step. A feature vector is estimated in a short time period of input speech signal to take the advantage of dependence on time domain. Finally, in the learning step, Hidden Markov Model (HMM) is used to create a model for each emotion class and recognize a particular input emotional speech. In the experiment, feature extraction based on Gammatone filterbank (GTF) shows the better outcomes in comparison with features based on Mel-Frequency Cepstral Coefficient (MFCC) which is a well-known feature extraction for speech recognition as well as emotion recognition from speech.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.470-473
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• 2004

In this paper, the comparison of speech feature parameters for emotion recognition is studied for emotion recognition using speech signal. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy. MFCC parameters and their derivatives with or without cepstral mean subfraction are also used to evaluate the performance of the conventional pattern matching algorithms. Pitch and energy Parameters were used as a Prosodic information and MFCC Parameters were used as phonetic information. In this paper, In the Experiments, the vector quantization based emotion recognition system is used for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy parameters. The vector quantization based emotion recognizer achieved recognition rates of 73.3％ for the speaker and context independent classification.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.

• MALSORI
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• no.62
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• pp.19-32
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• 2007

This study investigated the perception of emotional voices by transplanting some or all of the prosodic aspects, i.e. pitch, duration, and intensity, of the utterances produced with emotional voices onto those with normal voices and vice versa. Listening evaluation by 24 raters revealed that prosodic effect was greater than segmental & vocal quality effect on the preception of the emotion. The degree of influence of prosody and that of segments & vocal quality varied according to the type of emotion. As for fear, prosodic elements had far greater influence than segmental & vocal quality elements whereas segmental and vocal elements had as much effect as prosody on the perception of happy voices. Different amount of contribution to the perception of emotion was found among prosodic features with the descending order of pitch, duration and intensity. As for the length of the utterances, the perception of emotion was more effective with long utterances than with short utterances.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.