Epoch extraction from linear prediction residual for identification of closed glottis interval

Ananthapadmanabha, T.; Yegnanarayana, B.

doi:10.1109/tassp.1979.1163267

Cited by 239 publications

(110 citation statements)

References 15 publications

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“…Speech signal energy level is also low prior to the release of a stop sound and also in some fricative sounds. Even within a glottal cycle of a voiced speech signal the energy of the signal is usually higher only in the vicinity of the major excitation of the vocal tract system, which is the instant of glottal closure in each glottal cycle (Ananthapadmanabha and Yegnanarayana, 1979). This is due to damped sinusoidal nature of the impulse response of the vocal tract system.…”

Section: E Ects Of Noise On the Speech Signalmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

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In this paper we propose a method for enhancement of speech in the presence of additive noise. The objective is to selectively enhance the high signal-to-noise ratio (SNR) regions in the noisy speech in the temporal and spectral domains, without causing signi®cant distortion in the resulting enhanced speech. This is proposed to be done at three di erent levels. (a) At the gross level, by identifying the regions of speech and noise in the temporal domain. (b) At the ®ner level, by identifying the regions of high and low SNR portions in the noisy speech. (c) At the short-time spectrum level, by enhancing the spectral peaks over spectral valleys. The basis for the proposed approach is to analyze linear prediction (LP) residual signal in short (1±2 ms) segments to determine whether a segment belongs to a noise region or speech region. In the speech regions the inverse spectral¯atness factor is signi®cantly higher than in the noisy regions. The LP residual signal enables us to deal with short segments of data due to uncorrelatedness of the samples. Processing of noisy speech for enhancement involves mostly weighting the LP residual signal samples. The weighted residual signal samples are used to excite the time-varying all-pole ®lter to produce enhanced speech. As the additive noise level in the speech signal is increased, the quality of the resulting enhanced speech decreases progressively due to loss of speech information in the low SNR, high noise regions. Thus the degradation in performance of enhancement is graceful as the overall SNR of the noisy speech is decreased. Ó

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Section: E Ects Of Noise On the Speech Signalmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

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“…It is used for the extraction of the spectral envelope of speech in compact form [8]. The basic idea of LPC is that the current speech sample may be approximated as a linear combination of few past samples [9] [10].…”

Section: Linear Predictive Codingmentioning

confidence: 99%

Effect of Glottal Excitation Interchange in Hindi and Dogri Languages

Mahajan¹,

Mehra²,

Lehana³

2015

IJCA

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Voice acoustics is an active area of research which studies speaking voice and has gain popularity due to rapid advancements in digital signal processing. The shape of glottal excitation and the vocal tract may be speaker and language dependent. The objective of this paper is to study the effect of glottal excitation interchange on the quality and intelligibility in Hindi and Dogri languages. For this, recordings of six speakers (3 males and 3 females) were carried out in Dogri and Hindi languages. Cardinal vowels (/a/, /i/, /u/) were extracted from recordings of each speaker. Investigations were carried out by interchanging the glottal excitations corresponding to the vowels in the two languages for each speaker. The analysis of the results showed that interchange of excitation does not provide satisfactory quality of the synthesized speech in terms of identity and clarity of speech. Further, the synthesized speech is perceived as it was spoken in the original language. It was also observed that if any two of the parameters (excitation, gain, vocal tract LPC coefficients) are interchanged, the accent of the original language also changes. It means that minimum two of the three parameters are necessary to interchange for modifying the accent of the language under consideration.

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“…We previously introduced the GLOMM method [5], which was based on detecting glottal events (glottal opening/closing) by detecting times of high linear prediction error. Rather than attempt to reconstruct the glottal source waveform accurately by separately modeling open and closed glottis regions [6], [7], GLOMM simply models the data as a recurrent pulse shape (located at the glottal event times) driving a (slowly) time-varying allpole filter. The filter (linear prediction coefficients) and the recurrent pulse shape are estimated in alternation.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

Glottal mixture model (GLOMM) for speaker identification on telephone channels

Baggenstoss

Wilkinghoff

Kurth

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The Glottal Mixture Model (GLOMM) extracts speaker-dependent voice source information from speech data. It has previously been shown to provide speaker identification performance on clean speech comparable to universal background model (UBM), a state of the art method based on MFCC. And, when combined with UBM, the error rate was reduced by a factor of three, showing that the voice source information is largely independent of the information contained in the MFCC, yet holds as much speaker-related information. We now describe how GLOMM can be adapted for telephone quality audio and provide significant error reduction when combined with UBM and I-vector approaches. We demonstrate a factor of two error reduction on the NTIMIT data set with respect to the best published results.

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Epoch extraction from linear prediction residual for identification of closed glottis interval

Abstract: of the vocal tract system and the glottal pulses Show more

Cited by 239 publications

References 15 publications

Speech enhancement using linear prediction residual

Speech enhancement using linear prediction residual

Effect of Glottal Excitation Interchange in Hindi and Dogri Languages

Glottal mixture model (GLOMM) for speaker identification on telephone channels

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