• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.221-224
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• 1995

In speech signal processing, it is necessary to detect exactly the pitch. The algorithms of pitch extraction with have been proposed until now are difficult to detect pitches over wide range speech signals. In this paper, thus, we proposed a new pitch detection algorithm that used a low pass filter with variable bandwidth. It is the method that preprosses to find the first formant of speech signals by the FFT at each frame and detects the pitches for signals LPFed with the cut off frequency according to the first formant. Applying the method, we obtained the pitch contours, improving the accuracy of pitch detection in some noise environments.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2414-2416
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• 1998

This paper presents real-time implementation of speech vocoder for PSTN video telephony using ITU G.723 16Kbps ADPCM algorithm. The ADPCM encoder accepts 8-bit PCM compressed signals and expends it to a 14-bit-per-sample. The predicted values are subtracted from encoded signals to produce difference signals. Adaptive quantization is performed on the difference signal to produce a 2-bit, output for transmission over the channel. Computer simulations and experiments were performed to evaluate the performance of the speech vocoder.

• ETRI Journal
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• v.38 no.6
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• pp.1064-1073
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• 2016

An adaptive speech streaming method to improve the perceived speech quality of a software-based multipoint control unit (SW-based MCU) over IP networks is proposed. First, the proposed method predicts whether the speech packet to be transmitted is lost. To this end, the proposed method learns the pattern of packet losses in the IP network, and then predicts the loss of the packet to be transmitted over that IP network. The proposed method classifies the speech signal into different classes of silence, unvoiced, speech onset, or voiced frame. Based on the results of packet loss prediction and speech classification, the proposed method determines the proper amount and bitrate of redundant speech data (RSD) that are sent with primary speech data (PSD) in order to assist the speech decoder to restore the speech signals of lost packets. Specifically, when a packet is predicted to be lost, the amount and bitrate of the RSD must be increased through a reduction in the bitrate of the PSD. The effectiveness of the proposed method for learning the packet loss pattern and assigning a different speech coding rate is then demonstrated using a support vector machine and adaptive multirate-narrowband, respectively. The results show that as compared with conventional methods that restore lost speech signals, the proposed method remarkably improves the perceived speech quality of an SW-based MCU under various packet loss conditions in an IP network.

• Speech Sciences
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• v.2
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• pp.109-124
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• 1997

This paper shows how a distinctive feature model can effectively be implemented into speech understanding within the framework of the Optimality Theory(OT); i.e., to show how distinctive features can optimally be extracted from given speech signals, and how segments can be chosen as the optimal ones among plausible candidates. This paper will also show how the sequence of segments can successfully be matched with optimal words in a lexicon.

• ETRI Journal
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• v.36 no.5
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• pp.721-729
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• 2014

We propose a novel phase-based method for single-channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase-dependent a priori signal-to-noise ratio (SNR) is estimated in the log-mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase-dependent estimator is incorporated into the conventional magnitude-based decision-directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one-frame delay of the estimated phase-dependent a priori SNR by using a minimum mean square error (MMSE)-based and maximum a posteriori (MAP)-based estimator. In our speech enhancement experiments, the proposed phase-dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE-based and MAP-based estimator cases as compared to a conventional magnitude-based estimator.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1756-1760
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• 2015

This paper focuses on a priori signal to noise ratio (SNR) estimation method for the speech enhancement. There are many researches for speech enhancement with several ambient noise cancellation methods. The method based on spectral subtraction (SS) which is widely used in noise reduction has a trade-off between the performance and the distortion of the signals. So the need of adaptive method like an estimated a priori SNR being able to making a high performance and low distortion is increasing. The decision directed (DD) approach is used to determine a priori SNR in noisy speech signals. A priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a modified a priori SNR estimator and the weighted rational transfer function for speech enhancement with wind noises. The experimental result shows the performance of our proposed estimator is better Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862) compare to the conventional DD approach-based systems and different noise reduction methods.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.18 no.1
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• pp.22-25
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• 2007

Background and Objectives: Biomedical signals have been usually used for the diagnosis of the laryngeal function such as speech, electroglottograph(EGG), airflow and other signals. But, in most cases these signals were analysed separately. Here, we propose a new interchannel parameter Glottal Closure Delay Ratio(GCDR) which is estimated from speech and EGG measured simultaneously. Materials and Method: Speech and EGG signal were recorded simultaneously from 13 normal subjects, 39 patients. The patients' data included 16 polyps and 23 vocal folds palsy. Time difference between glottal closing instance on EGG and the first maximum peak on speech in a pitch period was calculated. Glottal closing instance was defined as the maximum peak on the first derivative of EGG signal(dEGG). Results: The standard deviation and jitter were calculated using 20-30 GCDRs extracted from each data, and they are significant different between normal and vocal fold paralysis group. Conclusion: The GCDR may be the first index reflecting speech and EGG characteristics and the perturbation of this parameter was significant different between normal and vocal fold paralysis group.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.89-92
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• 2005

Using natural speech commands for controlling a human-robot is an interesting topic in the field of robotics. In this paper, our main focus is on the verification of a speaker who gives a command to decide whether he/she is an authorized person for commanding. Among possible dynamic features of natural speech, pitch period is one of the most important ones for characterizing speech signals and it differs usually from person to person. However, current techniques of pitch detection are still not to a desired level of accuracy and robustness. When the signal is noisy or there are multiple pitch streams, the performance of most techniques degrades. In this paper, we propose a two-level approach for pitch detection which in compare with standard pitch detection algorithms, not only increases accuracy, but also makes the performance more robust to noise. In the first level of the proposed approach we discriminate voiced from unvoiced signals based on a neural classifier that utilizes cepstrum sequences of speech as an input feature set. Voiced signals are then further processed in the second level using a modified standard AMDF-based pitch detection algorithm to determine their pitch periods precisely. The experimental results show that the accuracy of the proposed system is better than those of conventional pitch detection algorithms for speech signals in clean and noisy environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.811-816
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• 2019

This paper first proposes a technique to separate clean speech signals and mixed noise signals by using an independent vector analysis algorithm of frequency band for 4 channel speech source signals with a noise. An improved output speech signal from the proposed independent vector analysis algorithm is obtained by using the cross-correlation between the signal outputs from the frequency domain delay-sum beamforming and the output signals separated from the proposed independent vector analysis algorithm. In the experiments, the proposed algorithm improves the maximum SNRs of 10.90dB and the segmental SNRs of 10.02dB compared with the frequency domain delay-sum beamforming algorithm for the input mixed noise speeches with 0dB and -5dB SNRs including white noise, respectively. Therefore, it can be seen from this experiment and consideration that the speech quality of this proposed algorithm is improved compared to the frequency domain delay-sum beamforming algorithm.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.101-109
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• 2003

A multi-mode harmonic transform coding (MMHTC) for speech and music signals is proposed. Its structure is organized as a linear prediction model with an input of harmonic and transform-based excitation. The proposed coder also utilizes harmonic prediction and an improved quantizer of excitation signal. To efficiently quantize the excitation of music signals, the modulated lapped transform(MLT) is introduced. In other words, the coder combines both the time domain (linear prediction) and the frequency domain technique to achieve the best perceptual quality. The proposed coder showed better speech quality than that of the 8 kbps QCELP coder at a bit-rate of 4 kbps.