• Speech Sciences
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• v.5 no.1
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• pp.63-80
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• 1999

In this paper, an adaptive method of dividing a speech signal into an initial, a medial and a final sound of the form of utterance utilized by evaluating extreme limits of short term energy and autocorrelation functions. By applying this method into speech signal composed of a consonant, a vowel and a consonant, it was divided into an initial, a medial and a final sound and its feature analysis of sample by LPC were carried out. As a result of spectrum analysis in each period, it was observed that there existed spectrum features of a consonant and a vowel in the initial and medial periods respectively and features of both in a final sound. Also, when all kinds of words were adaptively divided into 3 periods by using the proposed method, it was found that the initial sounds of the same consonant and the medial sounds of the same vowels have the same spectrum characteristics respectively, but the final sound showed different spectrum characteristics even if it had the same consonant as the initial sound.

• 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.

• Journal of the Korean Society for Railway
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• v.14 no.2
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• pp.130-136
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• 2011

This paper suggests realization method for real-time speech recognition using VoiceXML in telephony environment based on SIP for Railroad Reservation Service. In this method, voice signal incoming through PSTN or Internet is treated as dialog using VoiceXML and the transferred voice signal is processed by Speech Recognition System, and the output is returned to dialog of VoiceXML which is transferred to users. VASR system is constituted of dialog server which processes dialog, APP server for processing voice signal, and Speech Recognition System to process speech recognition. This realizes transfer method to Speech Recognition System in which voice signal is recorded using Record Tag function of VoiceXML to process voice signal in telephony environment and it is played in real time.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.178-183
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• 2006

As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.150-155
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• 2006

Speech intelligibility is investigated on a conference room-window glass coupled system. Using MLS(Maximum Length Sequency) signal as a sound source, acceleration and velocity responses of the window glass are measured by accelerometer and laser doppler vibrometer. MTF(Modulation Transfer Function) is used to identify the speech transmission characteristics of the room and window system. STI(Speech Transmission Index) is calculated by using MTF and speech intelligibility of the room and the window glass is estimated. Speech intelligibilities by the acceleration signal and the velocity signal are compared and the possibility of the wiretapping is investigated. Finally, intelligibility of the conversation sound is examined by the subjective test.

• ETRI Journal
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• v.38 no.2
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• pp.366-375
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• 2016

This paper describes an algorithm to suppress composite noise in a two-microphone speech enhancement system for robust hands-free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal-dominant time-frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech-dominant TFBs are identified among the previously detected nonstationary signal-dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin-wise output signal-to-noise ratio is obtained with these power estimates and a Wiener post-filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post-filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.75-76
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• 2001

An endpoint detection method for speech signals utilizing hidden Markov model(HMM) is proposed. It turns out that the proposed algorithm is quite satisfactory to apply isolated word speech recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.175-182
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• 2010

In this paper, we propose a robust speech reinforcement technique to enhance the intelligibility of the degraded speech signal under the ambient noise environments based on soft decision scheme incorporating a speech absence probability (SAP) with speech reinforcement gains. Since the ambient noise significantly decreases the intelligibility of the speech signal, the speech reinforcement approach to amplify the estimated clean speech signal from the background noise environments for improving the intelligibility and clarity of the corrupted speech signal was proposed. In order to estimate the robust reinforcement gain rather than the conventional speech reinforcement method between speech active periods and nonspeech periods or transient intervals, we propose the speech reinforcement algorithm based on soft decision applying the SAP to the estimation of speech reinforcement gains. The performances of the proposed algorithm are evaluated by the Comparison Category Rating (CCR) of the measurement for subjective determination of transmission quality in ITU-T P.800 under various ambient noise environments and show better performances compared with the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.47-50
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• 2015

Speech Signals consist of signals of consonants and vowels, but the lasting time of vowels is much longer than that of consonants. It can be assumed that the correlations between signal blocks in speech signal is very high. Each speech signal is divided into blocks which have 128 speech data. FFT is applied to each block. Low frequency areas of the results of FFT is taken and Covariance matrix between blocks in a speech signal is extracted and finally eigenvalues of those matrix are obtained. It is studied that what the distribution of eigenvalues of various speech files is. The differences between speech signals and noise signals from cars are also studied.