The influence of glottal waveform on the naturalness of speech from a parallel formant synthesizer

Holmes, John

doi:10.1109/tau.1973.1162466

Cited by 91 publications

(31 citation statements)

References 6 publications

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“…In this area, knowledge achieved via GIF has been used in the forms of artificial glottal source models and voice source control rules that have been utilized in several studies during the past three decades (e.g., Klatt 1987;Carlson et al 1989;Pinto et al 1989;de Veth et al 1990;Klatt & Klatt 1990;Carlson et al 1991;Karlsson 1991;Karlsson 1992;Fant 1993;Childers & Hu 1994;Childers 1995). In addition to these earlier studies, where the synthesis is typically rule-based and utilizes, for instance, the LF-model, GIF has also been used as a sound synthesis technique, in which the glottal source extracted from real speech is used to excite artificial vocal tract models (Holmes 1973;Matsui et al 1991;Alku et al 1999). These synthesis techniques enable generation of high-quality sounds, and the vocal tract characteristics can be controlled.…”

Section: Applications Of Gifmentioning

confidence: 99%

Glottal inverse filtering analysis of human voice production — A review of estimation and parameterization methods of the glottal excitation and their applications

Alku

2011

Sadhana

168

144

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Glottal inverse filtering (GIF) refers to methods of estimating the source of voiced speech, the glottal volume velocity waveform. GIF is based on the idea of inversion, in which the effects of the vocal tract and lip radiation are cancelled from the output of the voice production mechanism, the speech signal. This article provides a review on GIF research by examining an era spanning five decades during which this topic has been under development. The topic is handled from three main perspectives: the estimation methods of the glottal source, the parameterization techniques that have been developed to express the estimated glottal excitations in numerical forms, and the application areas of GIF. Finally, the strengths and limitations of the GIF approach are discussed.

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Section: Applications Of Gifmentioning

confidence: 99%

Glottal inverse filtering analysis of human voice production — A review of estimation and parameterization methods of the glottal excitation and their applications

Alku

2011

Sadhana

168

144

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“…Other approaches to modeling the voice source include those motivated by physical modeling and include models such as Ishizaka and Flanagan [6] and Story and Titze [7]. The importance of accurately reproducing the voice source signal in speech synthesis is described in [8], where experimentation has shown that a parallel formant synthesizer can generate short speech segments indistinguishable from real speech provided it is driven by an inverse-filtered typical natural vowel from the same talker. A related approach is described in [9] where cepstrum coefficients are used to generate a single average voice source waveform from which any speech signal can be synthesized.…”

Section: Introductionmentioning

confidence: 99%

Data-driven voice source waveform analysis and synthesis

Guðnason

Thomas

Ellis

et al. 2012

Speech Communication

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The paper presents a voice source waveform modeling techniques based on principal component analysis (PCA) and Gaussian mixture modeling (GMM). The voice source is obtained by inverse-filteirng speech with the estimated vocal tract filter. This decomposition is useful in speech analysis, synthesis, recognition and coding. Existing models of the voice source signal are based on function-fitting or physically motivated assumptions and although they are well defined, estimation of their parameters is not well understood and few are capable of reproducing the large variety of voice source waveforms. Here, a data-driven approach is presented for signal decomposition and classification based on the principal components of the voice source. The principal components are analyzed and the 'prototype' voice source signals corresponding to the Gaussian mixture means are examined. We show how an unknown signal can be decomposed into its components and/or prototypes and resynthesized. We show how the techniques are suited for both low bitrate or high quality analysis/synthesis schemes.

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“…Early models of the source signal used a simple impulse train for modeling voiced signals. More recent studies model the shape of the glottal airflow or its derivative in the timedomain ͑Ananthapadmanabha, 1984; Fant et al, 1985;Hedelin, 1984;Holmes, 1973;Klatt and Klatt, 1990;Rosenberg, 1971͒. Frequency-domain representations for some of those models were presented in Fant ͑1995͒ and Doval and d'Alessandro ͑1999͒.…”

Section: Introductionmentioning

confidence: 99%

Age, sex, and vowel dependencies of acoustic measures related to the voice source

Iseli

Shue

Alwan

2007

The Journal of the Acoustical Society of America

195

119

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The effects of age, sex, and vocal tract configuration on the glottal excitation signal in speech are only partially understood, yet understanding these effects is important for both recognition and synthesis of speech as well as for medical purposes. In this paper, three acoustic measures related to the voice source are analyzed for five vowels from 3145 CVC utterances spoken by 335 talkers (8-39 years old) from the CID database [Miller et al., Proceedings of ICASSP, 1996, Vol. 2, pp. 849-852]. The measures are: the fundamental frequency (F0), the difference between the "corrected" (denoted by an asterisk) first two spectral harmonic magnitudes, H1* - H2* (related to the open quotient), and the difference between the "corrected" magnitudes of the first spectral harmonic and that of the third formant peak, H1* - A3* (related to source spectral tilt). The correction refers to compensating for the influence of formant frequencies on spectral magnitude estimation. Experimental results show that the three acoustic measures are dependent to varying degrees on age and vowel. Age dependencies are more prominent for male talkers, while vowel dependencies are more prominent for female talkers suggesting a greater vocal tract-source interaction. All talkers show a dependency of F0 on sex and on F3, and of H1* - A3* on vowel type. For low-pitched talkers (F0 < or = 175 Hz), H1* - H2* is positively correlated with F0 while for high-pitched talkers, H1* - H2* is dependent on F1 or vowel height. For high-pitched talkers there were no significant sex dependencies of H1* - H2* and H1* - A3*. The statistical significance of these results is shown.

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The influence of glottal waveform on the naturalness of speech from a parallel formant synthesizer

Cited by 91 publications

References 6 publications

Glottal inverse filtering analysis of human voice production — A review of estimation and parameterization methods of the glottal excitation and their applications

Glottal inverse filtering analysis of human voice production — A review of estimation and parameterization methods of the glottal excitation and their applications

Data-driven voice source waveform analysis and synthesis

Age, sex, and vowel dependencies of acoustic measures related to the voice source

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