• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.42-47
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• 1994

A pitch detector is an essential component in a variety of speech processing systems. Besides providing valuable insights into the nature of the exciation source for speech production, the pitch contour of an utterance is useful for recognizing speakers, aids-to-the handicapped, and is required in almost all speech analysis-synthesis system. Because of the importance of the pitch detection, a wide variety algorithms for pitch detection have been proposed in speech procesing literature. Thus, in this paper we discuss th evarious type of pitch detection algorithms which have been proposed until now. Then we provide th eperformance measurements for seven pitch detection algorithms.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.5-12
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• 1996

We propose a two-channel(Speech & EGG) pitch detection algorithm. The EGG signal monitors the vibratory motion of vocal folds very well. Therefore, using the EGG signal as well as speech signal, we obtain a reliable and robust pitch detection algorithm that minimizers problems occuring in the pitch detection with speech only. The proposed algorithm gives precise pitch markers that are synchronized to the speech in the time domain. Experimental results demonstrate the superiority of the two-channel pitch detection algorithm over the conventional method, and it can be used in obtaining reference pitch for evaluation of other pitch detection algorithms.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1797-1798
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• 2007

In field of voice pathology, diverse statistics extracted form pitch estimation were commonly used to assess voice quality. In this study, we proposed robust pitch detection algorithm which can estimate pitch of pathological voices in benign vocal fold lesions. we also compared our proposed algorithm with three established pitch detection algorithms; autocorrelation, simplified inverse filtering technique, and nonlinear state-space embedding methods. In the database of total pathological voices of 99 and normal voices of 30, an analysis of errors related with pitch detection was evaluated between pathological and normal voices, or among the types of pathological voices. According to the results of pitch errors, gross pitch error showed some increases in cases of pathological voices; especially excessive increase in PDA based on nonlinear time-series. In an analysis of types of pathological voices classified by aperiodicity and the degree of chaos, the more voice has aperiodic and chaotic, the more growth of pitch errors increased. Consequently, it is required to survey the severity of tested voice in order to obtain accurate pitch estimates.

• 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.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.381-384
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• 2002

In speech signal processing, it Is very important to detect the pitch exactly in speech recognition, synthesis and analysis. but, it is very difficult to pitch detection from speech signal because of formant and transition amplitude affect. therefore, in this paper, we proposed a pitch detection using the spectrum flattening techniques. Spectrum flattening is to eliminate the formant and transition amplitude affect. In time domain, positive center clipping is process in order to emphasize pitch period with a glottal component of removed vocal tract characteristic. And rough formant envelope is computed through peak-fitting spectrum of original speech signal in frequency domain. As a results, well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. After all, we obtain residual signal which is removed vocal tract element The performance was compared with LPC and Cepstrum, ACF 0wing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Speech Sciences
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• v.10 no.2
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.4
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• pp.38-44
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• 1980

We present a high speed pitch estimation algorithm that is based on peak detection and average magnitude difference function (AMDF). A few pitch candidates are first estimated from the low-pass filtered (800 Hz) speech by a peak detection algorithm. AMDF values of the pitch candidatestare then calculated, and the pitch candidate that yields the minimum AMDF value is chosen as the desired pitch period. The new method requires far less computation time than other pitch estimation algorithms, while it yields fairly accurate results.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.04a
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• pp.5-8
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• 1984

Several pitch detection algorithms are studied and compared with the standard pitch detector in a terms of some kinds of errors and each of speaders. Various types of errors are defined, and rank the performance of pitch detectors.

• ETRI Journal
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• v.25 no.1
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• pp.49-51
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• 2003

Pitch estimation is important in various speech research areas, but when the speech is noisy, accurate pitch estimation with conventional pitch detectors is almost impossible. To solve this problem, we propose a new pitch detection algorithm for noisy speech using a noise whitening technique on the background noise and obtain successful results.