A diphone synthesis system based on time-domain prosodic modifications of speech

Hamon, Christian; Mouline, E.; Charpentier, F.

doi:10.1109/icassp.1989.266409

Cited by 84 publications

(34 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coef®cients of the ®lter were used mainly to derive the noisy residual signal by inverse ®ltering. Retaining the waveform bells around the glottal closure produces good quality speech as was demonstrated in PSOLA based Text-to-Speech system (TTS) (Hamon et al, 1989).…”

Section: Nature Of Lp Residual Signalmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

View full text Add to dashboard Cite

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

show abstract

Section: Nature Of Lp Residual Signalmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

View full text Add to dashboard Cite

show abstract

“…With the knowledge of the epochs, it is possible to determine the characteristics of the voice source by a careful analysis of the signal within a glottal pulse. The epochs can be used as pitch markers for prosody manipulation, which is useful in applications like text-to-speech synthesis, voice conversion and speech rate conversion [3], [4]. Knowledge of the epoch locations may be used for estimating the time-delay between speech signals collected over a pair of spatially distributed microphones [5].…”

Section: A Significance Of Epochs In Speech Analysismentioning

confidence: 99%

Epoch Extraction From Speech Signals

Murty

Yegnanarayana

2008

IEEE Trans. Audio Speech Lang. Process.

556

288

View full text Add to dashboard Cite

Abstract-Epoch is the instant of significant excitation of the vocal-tract system during production of speech. For most voiced speech, the most significant excitation takes place around the instant of glottal closure. Extraction of epochs from speech is a challenging task due to time-varying characteristics of the source and the system. Most epoch extraction methods attempt to remove the characteristics of the vocal-tract system, in order to emphasize the excitation characteristics in the residual. The performance of such methods depends critically on our ability to model the system. In this paper, we propose a method for epoch extraction which does not depend critically on characteristics of the time-varying vocaltract system. The method exploits the nature of impulse-like excitation. The proposed zero resonance frequency filter output brings out the epoch locations with high accuracy and reliability. The performance of the method is demonstrated using CMU-Arctic database using the epoch information from the electro-glottograph as reference. The proposed method performs significantly better than the other methods currently available for epoch extraction. The interesting part of the results is that the epoch extraction by the proposed method seems to be robust against degradations like white noise, babble, high-frequency channel, and vehicle noise.

show abstract

“…This glottal pulse shape has also a position in time c i . Speech processing techniques often define c i as glottal closure instants [48,49], or as energy local maxima of a residual signal [50,51], or as pitch pulse onsets [12,14,27] for centering windows and to synchronize instantaneous phase parameters. Even though such a definition is necessary for many approaches, we will show below that it is not necessary when using the Relative Phase Shift (RPS) [24,33] or PD, which avoids an extra estimation procedure and its potential misestimation errors.…”

Section: Theoretical Model Of the Instantaneous Phasementioning

confidence: 99%

A uniform phase representation for the harmonic model in speech synthesis applications

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

show abstract

A diphone synthesis system based on time-domain prosodic modifications of speech

Cited by 84 publications

References 9 publications

Speech enhancement using linear prediction residual

Speech enhancement using linear prediction residual

Epoch Extraction From Speech Signals

A uniform phase representation for the harmonic model in speech synthesis applications

Contact Info

Product

Resources

About