• The Journal of the Acoustical Society of Korea
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• v.24 no.4E
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• pp.144-151
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• 2005

We evaluate the performance of emotion recognition via speech signals when a plain speaker talks to an entertainment robot. For each frame of a speech utterance, we extract the frame-based features: pitch, energy, formant, band energies, mel frequency cepstral coefficients (MFCCs), and velocity/acceleration of pitch and MFCCs. For discriminative classifiers, a fixed-length utterance-based feature vector is computed from the statistics of the frame-based features. Using a speaker-independent database, we evaluate the performance of two promising classifiers: support vector machine (SVM) and hidden Markov model (HMM). For angry/bored/happy/neutral/sad emotion classification, the SVM and HMM classifiers yield $42.3\%\;and\;40.8\%$ accuracy, respectively. We show that the accuracy is significant compared to the performance by foreign human listeners.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.259-266
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• 2014

In this paper, we propose a novel technique for noise robust automatic speech recognition (ASR). The development of ASR techniques has made it possible to recognize isolated words with a near perfect word recognition rate. However, in a highly noisy environment, a distinct mismatch between the trained speech and the test data results in a significantly degraded word recognition rate (WRA). Unlike conventional ASR systems employing Mel-frequency cepstral coefficients (MFCCs) and a hidden Markov model (HMM), this study employ histogram of oriented gradient (HOG) features and a Support Vector Machine (SVM) to ASR tasks to overcome this problem. Our proposed ASR system is less vulnerable to external interference noise, and achieves a higher WRA compared to a conventional ASR system equipped with MFCCs and an HMM. The performance of our proposed ASR system was evaluated using a phonetically balanced word (PBW) set mixed with artificially added noise.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.3-11
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• 1998

Zero-crossings with Peak amplitudes (ZCPA) model motivated by human auditory periphery was proposed to extract reliable features form speech signals even in noisy environments for robust speech recognition. In this paper, the performance of the ZCPA model is further improved by incorporating conventional speech processing techniques into the model output. Spectral and cepstral representations of the ZCPA model output are compared, and the incorporation of dynamic features with several different lengths of time-derivative window are evaluated. Also, comparative evaluations with other front-ends in real-world noisy environments are performed, and result in the superiority of the ZCPA model.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.245-246
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• 2006

In this paper, we proposed using MFCC coefficients(Mel-Scaled Cepstral Coefficients) and a simple but efficient classifying method. Many other features: IAV, zero crossing, LPCC, $\ldot$ and their derivatives are also tested and compared with MFCC coefficients in order to find the best combination. GMM and HMM (Discrete and Continuous Hidden Markov Model), are studied as well in the hope that the use of continuous distribution and the temporal evolution of this set of features will improve the quality of emotion recognition.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.522-525
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• 2009

This paper proposed the content-based method of classifying filthy Phornographic video, which causes a big problem of modern society as the reverse function of internet. Audio data was used to extract the features from Phornographic video. There are frequency spectrum, autocorrelation, and MFCC as the feature of audio used in this paper. The sound that could be filthy contents was extracted, and the Phornographic was classified by measuring how much percentage of relevant sound was corresponding with the whole audio of video. For the experiment on the proposed method, The efficiency of classifying Phornographic was measured on each feature, and the measured result and comparison with using multi features were performed. I can obtain the better result than when only one feature of audio was extracted, and used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.817-821
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• 2012

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.248-254
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• 2014

Among acoustic signal analysis tasks, acoustic context awareness is one of the most formidable tasks in terms of complexity since it requires sophisticated understanding of individual acoustic events. In conventional context awareness methods, individual acoustic event detection or recognition is employed to generate a relevant decision on the impending context. However this approach may produce poorly performing decision results in practical situations due to the possibility of events occurring simultaneously or the acoustically similar events that are difficult to distinguish with each other. Particularly, the babble noise acoustic event occurring at a bus or subway environment may create confusion to context awareness task since babbling is similar in any environment. Therefore in this paper, a frequency-cepstral feature vector is proposed to mitigate the confusion problem during the situation awareness task of binary decisions: bus or metro. By employing the Support Vector Machine (SVM) as the classifier, the proposed feature vector scheme is shown to produce better performance than the conventional scheme.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.31-39
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• 2010

In this study, we prepared the real environmental sound data sets arising from people's movement comprising 9 different environment types. The environmental sounds are pre-processed with pre-emphasis and Hamming window, then go into the classification experiments with the extracted features using MFCC (Mel-Frequency Cepstral Coefficients). The GMM (Gaussian Mixture Model) classifier without post processing tends to yield abruptly changing classification results since it does not consider the results of the neighboring frames. Hence we proposed the post processing methods which suppress abruptly changing classification results by taking the probability or the rank of the neighboring frames into account. According to the experimental results, the method using the probability of neighboring frames improve the recognition performance by more than 10% when compared with the method without post processing.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.497-503
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• 2011

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.83-92
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• 1996

This paper describes Lombard effect compensation and noise suppression so as to reduce speech recognition error in noisy environments. Lombard effect is represented by the variation of spectral envelope of energy normalized word and the variation of overall vocal intensity. The variation of spectral envelope can be compensated by linear transformation in cepstral domain. The variation of vocal intensity is canceled by spectral magnitude normalization. Spectral subtraction is use to suppress noise contamination, and band-pass filtering is used to emphasize dynamic features. To understand Lombard effect and verify the effectiveness of the proposed method, speech data are collected in simulated noisy environments. Recognition experiments were conducted with contamination by noise from automobile cabins, an exhibition hall, telephone booths in down town, crowded streets, and computer rooms. From the experiments, the effectiveness of the proposed method has been confirmed.