Glottal source-vocal tract acoustic interaction

Fant, Gunnar; Lin, Qing

doi:10.1121/1.2024357

Cited by 21 publications

(22 citation statements)

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“…Equations 2.8 and 2.14 show the duality of the time domain ripple and the formant modulation in the frequency domain. It should be noted that while the glottal flow waveform is primarily effected by the first formant, all of the formants are approximately equally effected by this formant modulation [17]. The influence on the glottal flow due to higher formants is less because they tend to be of lower amplitude, and with their higher bandwidths, they have decayed even more by the time the glottis opens.…”

Section: A( V(s T) S/c H(st) Use(s) -S2 + Bl(t)s + 12(t)'mentioning

confidence: 98%

“…For speakers whose arytenoid cartilages are spread apart, a more triangle shaped opening will occur. Van den Berg has proposed an empirical formula for relating the steady-state pressure drop across glottis to the glottal flow: 16) where AP is the pressure drop across the glottis, called the trans-glottal pressure, k is an experimentally determined constant, p is the density of air, U is the glottal flow,…”

Section: Motivation For the Use Of The Glottal Flow In Speaker Identimentioning

confidence: 99%

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Modeling of the glottal flow derivative waveform with application to speaker identification

Plumpe

Quatieri

Reynolds

1999

IEEE Trans. Speech Audio Process.

236

175

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Speech production has long been viewed as a linear filtering process, as described by Fant in the late 1950's [10]. The vocal tract, which acts as the filter, is the primary focus of most speech work. This thesis develops a method for estimating the source of speech, the glottal flow derivative. Models are proposed for the coarse and fine structure of the glottal flow derivative, accounting for nonlinear sourcefilter interaction, and techniques are developed for estimating the parameters of these models. The importance of the source is demonstrated through speaker identification experiments.The glottal flow derivative waveform is estimated from the speech signal by inverse filtering the speech with a vocal tract estimate obtained during the glottal closed phase. The closed phase is determined through a sliding covariance analysis with a very short time window and a one sample shift. This allows calculation of formant motion within each pitch period predicted by Ananthapadmanabha and Fant to be a result of nonlinear source-filter interaction during the glottal open phase [1]. By identifying the timing of formant modulation from the formant tracks, the timing of the closed phase can be determined. The glottal flow derivative is modeled using an LF model to capture the coarse structure, while the fine structure is modeled through energy measures and a parabolic fit to the frequency modulation of the first formant.The model parameters are used in the Reynolds Gaussian Mixture Model Speaker Identification system with excellent results for non-degraded speech. Each category of source features is shown to contain speaker dependent information, while the combination of source and filter parameters increases the overall accuracy for the system. For a large dataset, the coarse structure parameters achieve 60% accuracy, the fine structure parameters give 40% accuracy, and their combination yields 70% correct identification. When combined with vocal tract features, the accuracy increases to 93%, slightly above the accuracy achieved with just vocal tract information. On smaller datasets of telephone-degraded speech, accuracy increases up to 20% when source features are added to traditional mel-cepstral measures. Perhaps one of his best contributions is that he and I tend to think of things from different angles, hopefully I will carry his viewpoint along with my own after I leave MIT. I would also like to thank Doug Reynolds for helping me understand and use his speaker identification system. Thanks are also owed to all the members of the Speech System Technology Group who have helped me in so many ways. I would also like to thank all the people who thought it would be a shame for me to not get an advanced degree and all my friends in Seattle who signed their letters "quit school."These conflicting views enabled me to make up my own mind with a minimum of outside pressure.And, of course, I wish to thank the sponsors of my research and my time here at MIT. I would like to thank the EECS department for awarding me a fe...

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Section: A( V(s T) S/c H(st) Use(s) -S2 + Bl(t)s + 12(t)'mentioning

confidence: 98%

Section: Motivation For the Use Of The Glottal Flow In Speaker Identimentioning

confidence: 99%

Modeling of the glottal flow derivative waveform with application to speaker identification

Plumpe

Quatieri

Reynolds

1999

IEEE Trans. Speech Audio Process.

236

175

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show abstract

“…Traditional analysis has been guided by the longstanding linear source-filter theory ͑Fant, 1960͒, which assumes that the source and the filter operate independently, even though an explicit "correction" is given to the glottal waveform that carries vocal tract loading effects in the form of pulse skewing and formant ripple ͑Flanagan, 1968; Rothenberg, 1981;Fant, 1986, Fant andLin, 1987͒. With such a flow source correction, source and filter can be combined or recombined ͑as in analysis-synthesis͒ spectrally to produce the mouth output, but interactions that increase the amplitude of vocal fold vibration or destabilize the source ͑i.e., major bifurcations in tissue movement͒ cannot be treated easily with such corrections.…”

Section: Introductionmentioning

confidence: 99%

Modeling source-filter interaction in belting and high-pitched operatic male singing

Titze

Worley

2009

The Journal of the Acoustical Society of America

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Nonlinear source-filter theory is applied to explain some acoustic differences between two contrasting male singing productions at high pitches: operatic style versus jazz belt or theater belt. Several stylized vocal tract shapes ͑caricatures͒ are discussed that form the bases of these styles. It is hypothesized that operatic singing uses vowels that are modified toward an inverted megaphone mouth shape for transitioning into the high-pitch range. This allows all the harmonics except the fundamental to be "lifted" over the first formant. Belting, on the other hand, uses vowels that are consistently modified toward the megaphone ͑trumpet-like͒ mouth shape. Both the fundamental and the second harmonic are then kept below the first formant. The vocal tract shapes provide collective reinforcement to multiple harmonics in the form of inertive supraglottal reactance and compliant subglottal reactance. Examples of lip openings from four well-known artists are used to infer vocal tract area functions and the corresponding reactances.

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“…Although the vowelspecific spectral slope can be predicted from the complex interaction between glottal source and vocal tract filtering ͑e.g., Fant, 1956;Fant et al, 1963;Fant and Lin, 1987͒, the size of the filter-related size variation in formant amplitude such as that found in vowels in context has not yet been integrated into the models. Yet, such spectral details may bolster speech production models, leading also to improved speech recognition schemes.…”

Section: Introductionmentioning

confidence: 99%

Amplitude variations in coarticulated vowels

Jacewicz

Fox

2008

The Journal of the Acoustical Society of America

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This paper seeks to characterize the nature, size, and range of acoustic amplitude variation in naturally produced coarticulated vowels in order to determine its potential contribution and relevance to vowel perception. The study is a partial replication and extension of the pioneering work by House and Fairbanks ͓J. Acoust. Soc. Am. 22, 105-113 ͑1953͔͒, who reported large variation in vowel amplitude as a function of consonantal context. Eight American English vowels spoken by men and women were recorded in ten symmetrical CVC consonantal contexts. Acoustic amplitude measures included overall rms amplitude, amplitude of the rms peak along with its relative location in the CVC-word, and the amplitudes of individual formants F1-F4 along with their frequencies. House and Fairbanks' amplitude results were not replicated: Neither the overall rms nor the rms peak varied appreciably as a function of consonantal context. However, consonantal context was shown to affect significantly and systematically the amplitudes of individual formants at the vowel nucleus. These effects persisted in the auditory representation of the vowel signal. Auditory spectra showed that the pattern of spectral amplitude variation as a function of contextual effects may still be encoded and represented at early stages of processing by the peripheral auditory system.

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Glottal source-vocal tract acoustic interaction

Cited by 21 publications

References 0 publications

Modeling of the glottal flow derivative waveform with application to speaker identification

Modeling of the glottal flow derivative waveform with application to speaker identification

Modeling source-filter interaction in belting and high-pitched operatic male singing

Amplitude variations in coarticulated vowels

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