Determination of instants of significant excitation in speech using group delay function

Smits, Roel; Yegnanarayana, B.

doi:10.1109/89.466662

Cited by 176 publications

(121 citation statements)

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“…From the clean speech the voiced/ unvoiced/silence segments and the instants of signi®cant excitation were identi®ed (Smits and Yegnanarayana, 1995;Yegnanarayana and Teunen, 1994). The LP residual signal of noisy speech was modi®ed retaining only the 2 ms portions of the residual signal around the instants of excitation.…”

Section: Nature Of Lp Residual 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: Nature Of Lp Residual Signalmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

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“…The instants of significant excitation can be derived from the LP residual signal as follows: Around each sample a 10 ms segment of the LP residual signal is considered, and the group delay function τ (ω) is computed using [18] filter to remove any discontinuities in the group delay function. The negative of the average of the smoothed group delay function is called phase slope [17]. The phase slope value is computed at each sampling instant to obtain the phase slope function.…”

Section: Proposed Methods For Duration Modificationmentioning

confidence: 99%

“…The instants of significant excitation (GCIs) are computed from the LP residual using group delay analysis [17]. The instants of significant excitation can be derived from the LP residual signal as follows: Around each sample a 10 ms segment of the LP residual signal is considered, and the group delay function τ (ω) is computed using [18] filter to remove any discontinuities in the group delay function.…”

Section: Proposed Methods For Duration Modificationmentioning

confidence: 99%

Duration modification using glottal closure instants and vowel onset points

Rao

Yegnanarayana

2009

Speech Communication

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This paper proposes a method for duration (time scale) modification using Glottal Closure Instants (GCI, also known as instants of significant excitation) and Vowel Onset Points (VOP). In general, most of the time scale modification methods attempt to vary the duration of speech segments uniformly over all regions. But it is observed that consonant regions and transition regions between a consonant and the following vowel, and between two consonant regions do not vary appreciably with speaking rate. The proposed method implements the duration modification without changing the durations of the transition and consonant regions. Vowel onset points are used to identify the transition and consonant regions. A VOP is the instant at which the onset of the vowel takes place, which corresponds to the transition from a consonant to the following vowel in most cases. The VOPs are computed using the Hilbert envelope of Linear Prediction (LP) residual. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations, like the onset of burst, in the case of nonvoiced speech. Manipulation of duration is achieved by modifying the duration of the LP residual with the help of instants of significant excitation as pitch markers. The modified residual is used to excite the time-varying filter whose parameters are derived from the original speech signal. Perceptual quality of the synthesized speech is found to be natural. Performance of the proposed method is compared with the method, where the duration of speech is modified uniformly over all regions. Samples of speech signals for different modification factors is available for listening at

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“…The authors reported significant improvements in performance, computational complexity and noise robustness. The method proposed in [8] estimates the location of the excitation within an analysis frame as the average value of the group delay. Recently, work on energy flow in the lossless-tube model has been reported [10] and it was suggested that the signal representing acoustic input power at the glottis can be used to determine the instants of glottal closure and opening.…”

Section: Segmentation Of the Larynx Cyclementioning

confidence: 99%

“…In this paper, the APLAWD database [11] has been used to perform comparative evaluations of three methods for estimating GCIs in voiced speech using Wong's LPC residual (LPCR) [6], the Frobenius Norm (FN) [7] and the Group Delay (GD) [8]. APLAWD contains phonetically balanced speech from 5 male and 5 female talkers as well as EGG recordings from which, after time-alignment, reference GCIs have been extracted using the HQTx algorithm [9].…”

Section: Segmentation Of the Larynx Cyclementioning

confidence: 99%

The DYPSA algorithm for estimation of glottal closure instants in voiced speech

Kounoudes¹,

Naylor²,

Brookes³

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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We present the DYPSA algorithm for automatic and reliable estimation of glottal closure instants (GCIs) in voiced speech. Reliable GCI estimation is essential for closed-phase speech analysis, from which can be derived features of the vocal tract and, separately, the voice source. It has been shown that such features can be used with significant advantages in applications such as speaker recognition. DYPSA is automatic and operates using the speech signal alone without the need for an EGG or Laryngograph signal. It incorporates a new technique for estimating GCI candidates and employs dynamic programming to select the most likely candidates according to a defined cost function. We review and evaluate three existing methods and compare our new algorithm to them. Results for DYPSA show GCI detection accuracy to within ±0.25ms on 87% of the test database and fewer than 1% false alarms and misses.

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Determination of instants of significant excitation in speech using group delay function

Cited by 176 publications

References 8 publications

Speech enhancement using linear prediction residual

Speech enhancement using linear prediction residual

Duration modification using glottal closure instants and vowel onset points

The DYPSA algorithm for estimation of glottal closure instants in voiced speech

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