• Title/Summary/Keyword: speech rate characteristic

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Text-Independent Speaker Identification System Based On Vowel And Incremental Learning Neural Networks

  • Heo, Kwang-Seung;Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1042-1045
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    • 2003
  • In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

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The Prosodic Characteristics of Pre-school Age Children-Related Adults (학령전기아동 관련 성인의 운율 특성)

  • Kim, Jiwon;Seong, Cheoljae
    • Phonetics and Speech Sciences
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    • v.6 no.3
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    • pp.23-32
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    • 2014
  • This study presents the prosodic characteristics of 'Motherese' and 'Teacherese (child care teacher and kindergarten teacher)'. 21 mothers and 24 teachers spoke to children in the child care center or kindergarten. Children are in their 4;00-6;11. Speech and articulation rate, number of accentual phrases (APs), number of intonational phrases (IPs), pitch-related factors (f0, pitch range, f0 standard deviation), and intonation slope (mean Absolute, f0, q-tone slope) were measured. 2 groups spoke 2 sentential types (interrogative_ alternative question, declarative_ coordinated sentence) in 2 situations (one accompanied with the children, the other done without children, but pretending as if they were in front of the children). The results indicate that teachers show more noticeable prosodic characteristics than mothers do.

Robust Feature Extraction for Voice Activity Detection in Nonstationary Noisy Environments (음성구간검출을 위한 비정상성 잡음에 강인한 특징 추출)

  • Hong, Jungpyo;Park, Sangjun;Jeong, Sangbae;Hahn, Minsoo
    • Phonetics and Speech Sciences
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    • v.5 no.1
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    • pp.11-16
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    • 2013
  • This paper proposes robust feature extraction for accurate voice activity detection (VAD). VAD is one of the principal modules for speech signal processing such as speech codec, speech enhancement, and speech recognition. Noisy environments contain nonstationary noises causing the accuracy of the VAD to drastically decline because the fluctuation of features in the noise intervals results in increased false alarm rates. In this paper, in order to improve the VAD performance, harmonic-weighted energy is proposed. This feature extraction method focuses on voiced speech intervals and weighted harmonic-to-noise ratios to determine the amount of the harmonicity to frame energy. For performance evaluation, the receiver operating characteristic curves and equal error rate are measured.

Flattening Techniques for Pitch Detection (피치 검출을 위한 스펙트럼 평탄화 기법)

  • 김종국;조왕래;배명진
    • 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.

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A Study on the Pitch Detection of Speech Harmonics by the Peak-Fitting (음성 하모닉스 스펙트럼의 피크-피팅을 이용한 피치검출에 관한 연구)

  • Kim, Jong-Kuk;Jo, Wang-Rae;Bae, Myung-Jin
    • 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.

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Improving The Excitation Signal for Low-rate CELP Speech Coding (저전송속도 CELP 부호화기에서 여기신호의 개선)

  • 권철홍
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1998.08a
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    • pp.136-141
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    • 1998
  • In order to enhance the performance of a CELP coder at low bit rates, it would be necessary to make the CELP excitation have the peaky pulse characteristic. In this paper we introduce an excitation signal with peaky pulse characteristic. It is obtained by using a two-tap pitch predictor. Samples of the signal have different gains according to their amplitudes by the predictor. In voiced sound the signal has the desirable peaky pulse characteristic, and its periodicity is well reproduced. Particularly, peaky pulses at voiced onset and a burst of plosive sound are clearly reconstructed.

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A Speech Coder using the Simplified Multi-mode Method (단순화된 다중 모드 방법을 이용한 음성 부호화기)

  • 강홍구
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1995.06a
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    • pp.146-149
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    • 1995
  • This paper proposes a SM-CELP speech coder which applies different excitation signal according to the characteristic of speech segment at bit-rate below 4 kbps. Speech signal is divided with 2 modes such as stationary voice and etc. using the parameters of average energy of the short-time speech and the residual signal after long term prediction. Structured multi-pulse method is used for the excitation of mode-A and gaussian or pulse-like codebook for mode-B. 4.8kbps DoD-CELP are used to evaluate the performance of the proposed coder. As a result, the propose method shows 1~2 dB higher segmental signal to noise ratio and better subjectional quality without increasing the computational amount.

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A Study on Speech Recognition based on Phoneme for Korean Subway Station Names (한국의 지하철역명을 위한 음소 기반의 음성인식에 관한 연구)

  • Kim, Beom-Seung;Kim, Soon-Hyob
    • Journal of the Korean Society for Railway
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    • v.14 no.3
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    • pp.228-233
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    • 2011
  • This paper presented the method about the Implementation of Speech Recognition based on phoneme considering the phonological characteristic for Korean Subway Station Names. The Pronunciation dictionary considering PLU set and phonological variations with four Case in order to select the optimum PLU used for Speech Recognition based on phoneme for Korean Subway Station Names was comprised and the recognition rate was estimated. In the case of the applied PLU, we could know the optimum recognition rate(97.74%) be shown in the triphone model in case of considering the recognition unit division of the initial consonant and final consonant and phonological variations.

A Study on Speaker Recognition Using MFCC Parameter Space (파마메터 공간을 이용한 화자인식에 관한 연구)

  • Lee Yong-woo;Lim dong-Chol;Lee Haing Sea
    • Proceedings of the Acoustical Society of Korea Conference
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    • autumn
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    • pp.57-60
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    • 2001
  • This paper reports on speaker-Recognition of context independence-speaker recognition in the field of the speech recognition. It is important to select the parameter reflecting the characteristic of each single person because speaker-recognition is to identify who speaks in the database. We used Mel Frequency Cesptrum Coefficient and Vector Quantization to identify in this paper. Specially, it considered to find characteristic-vector of the speaker in different from known method; this paper used the characteristic-vector which is selected in MFCC Parameter Space. Also, this paper compared the recognition rate according to size of codebook from this database and the time needed for operation with the existing one. The results is more improved $3\sim4\%$ for recognition rate than established Vector Quantization Algorithm.

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Voice Activity Detection Based on Entropy in Noisy Car Environment (차량 잡음 환경에서 엔트로피 기반의 음성 구간 검출)

  • Roh, Yong-Wan;Lee, Kue-Bum;Lee, Woo-Seok;Hong, Kwang-Seok
    • Journal of the Institute of Convergence Signal Processing
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    • v.9 no.2
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    • pp.121-128
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    • 2008
  • Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

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