• 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.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.253-256
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• 2017

This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.

• MALSORI
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• v.55
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• pp.61-76
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• 2005

This paper aims to analyze the acoustic effects of vowels produced in a distant-talking environment. The analysis was performed using a statistical method. The influence of gender and speakers on the variation was also examined. The speech data used in this study consist of 500 distant-talking words and 500 normal words of 10 speakers (5 males and 5 females). Acoustic features selected for the analysis were the duration, the formants (Fl and F2), the fundamental frequency and the total energy. The results showed that the duration, F0, F1 and the total energy increased in the distant-talking speech compared to normal speech; female speakers showed higher increase in all features except for the total energy and the fundamental frequency. In addition, speaker differences were observed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.150-154
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.234-239
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• 2007

The purpose of this paper is to examine the viability of simulating one dialect with the speech segments of another dialect through prosody cloning. The hypothesis is that, among Korean regional dialects, it is not the segmental differences but the prosodic differences that play a major role in authentic dialect perception. This work intends to support the hypothesis by simulating Masan dialect with the speech segments from Seoul dialect. The dialect simulation was performed by transplanting the prosodic features of Masan utterances unto the same utterances produced by a Seoul speaker. Thus, the simulated Masan utterances were composed of Seoul speech segments but their prosody came from the original Masan utterances. The prosodic features involved were the fundamental frequency contour, the segmental durations, and the intensity contour. The simulated Masan utterances were evaluated by four native Masan speakers and the role of prosody in dialect authentication and speech synthesis was discussed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.67-77
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• 2004

The recent research has been focused on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network(BMNN) is a multi-layer perception of 4 layers, each of which performs a certain level of abstraction of input features. In BMNN the third layer combines audio md visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition. Our research demonstrates that diverse sources of information need to be integrated to improve the accuracy of speech recognition particularly in noisy environments.

• MALSORI
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• no.57
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• pp.139-152
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• 2006

In this paper, we present a phrase break prediction method using CRF(Conditional Random Fields), which has good performance at classification problems. The phrase break prediction problem was mapped into a classification problem in our research. We trained the CRF using the various linguistic features which was extracted from POS(Part Of Speech) tag, lexicon, length of word, and location of word in the sentences. Combined linguistic features were used in the experiments, and we could collect some linguistic features which generate good performance in the phrase break prediction. From the results of experiments, we can see that the proposed method shows improved performance on previous methods. Additionally, because the linguistic features are independent of each other in our research, the proposed method has higher flexibility than other methods.

• ETRI Journal
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• v.36 no.5
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• pp.772-782
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• 2014

Two-microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new system that is effective in both directional and diffuse noise conditions. The system exploits two features. The first determines whether a given time-frequency (T-F) unit of the input spectrum is dominated by a diffuse or directional source. A diffuse signal is certainly a noise signal, but a directional signal could correspond to a noise or speech source. The second feature discriminates between T-F units dominated by speech or directional noise signals. Speech enhancement is performed using a binary mask, calculated based on the proposed features. In both directional and diffuse noise fields, the proposed system segregates speech T-F units with hit rates above 85%. It outperforms previous solutions in terms of signal-to-noise ratio and perceptual evaluation of speech quality improvement, especially in diffuse noise conditions.

• Phonetics and Speech Sciences
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• v.15 no.1
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• pp.47-54
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• 2023

This study attempted to test the hypothesis that Korean evaluators can score L2 speech appropriately, even when speech rate features are unavailable. Two perception experiments-preliminary and main-were conducted sequentially. The purpose of the preliminary experiment was to categorize English-as-a-foreign-language (EFL) speakers into two groups-advanced learners and lower-level learners-based on the proficiency scores given by five human raters. In the main experiment, a set of stimuli was prepared such that the speech rate of all data tokens was modified to have a uniform speech rate. Ten human evaluators were asked to score the stimulus tokens on a 5-point scale. These scores were statistically analyzed to determine whether there was a significant difference in utterance production between the two groups. The results of the preliminary experiment confirm that higher-proficiency learners speak faster than lower-proficiency learners. The results of the main experiment indicate that under controlled speech-rate conditions, human raters can appropriately assess learner proficiency, probably thanks to the linguistic features that the raters considered during the evaluation process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.122-126
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• 2002

In this study, we find features of 3 emotions (Happiness, Angry, Surprise) as the fundamental research of emotion recognition. Speech signal with emotion has several elements. That is, voice quality, pitch, formant, speech speed, etc. Until now, most researchers have used the change of pitch or Short-time average power envelope or Mel based speech power coefficients. Of course, pitch is very efficient and informative feature. Thus we used it in this study. As pitch is very sensitive to a delicate emotion, it changes easily whenever a man is at different emotional state. Therefore, we can find the pitch is changed steeply or changed with gentle slope or not changed. And, this paper extracts formant features from speech signal with emotion. Each vowels show that each formant has similar position without big difference. Based on this fact, in the pleasure case, we extract features of laughter. And, with that, we separate laughing for easy work. Also, we find those far the angry and surprise.