Simulation of Losses Due to Turbulence in the Time-Varying Vocal System

Birkholz, Peter; Jackèl, Dr.-Ing. habil. Dietmar; Kröger, Bernd J.

doi:10.1109/tasl.2006.889731

Cited by 47 publications

(32 citation statements)

References 33 publications

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“…The aeroacoustic simulation is based on a transmission-line circuit representation of the tube system (see [30] for a detailed derivation). The simulation considers fluid dynamic losses at constrictions, as well as losses due to radiation, soft walls, and viscous friction.…”

Section: Vocaltractlab the Articulatory Synthesizermentioning

confidence: 99%

Identifying underlying articulatory targets of Thai vowels from acoustic data based on an analysis-by-synthesis approach

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Birkholz

2014

J AUDIO SPEECH MUSIC PROC.

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This paper investigates the estimation of underlying articulatory targets of Thai vowels as invariant representation of vocal tract shapes by means of analysis-by-synthesis based on acoustic data. The basic idea is to simulate the process of learning speech production as a distal learning task, with acoustic signals of natural utterances in the form of Mel-frequency cepstral coefficients (MFCCs) as input, VocalTractLab -a 3D articulatory synthesizer controlled by target approximation models as the learner, and stochastic gradient descent as the target training method. To test the effectiveness of this approach, a speech corpus was designed to contain contextual variations of Thai vowels by juxtaposing nine Thai long vowels in two-syllable sequences. A speech corpus consisting of 81 disyllabic utterances was recorded from a native Thai speaker. Nine vocal tract shapes, each corresponding to a vowel, were estimated by optimizing the vocal tract shape parameters of each vowel to minimize the sum of square error of MFCCs between original and synthesized speech. The stochastic gradient descent algorithm was used to iteratively optimize the shape parameters. The optimized vocal tract shapes were then used to synthesize Thai vowels both in monosyllables and in disyllabic sequences. The results, both numerically and perceptually, indicate that this model-based analysis strategy allows us to effectively and economically estimate the vocal tract shapes to synthesize accurate Thai vowels as well as smooth formant transitions between adjacent vowels.

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Section: Vocaltractlab the Articulatory Synthesizermentioning

confidence: 99%

Identifying underlying articulatory targets of Thai vowels from acoustic data based on an analysis-by-synthesis approach

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Birkholz

2014

J AUDIO SPEECH MUSIC PROC.

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“…VocalTractLab simulates the acoustic signal by approximating the trachea, the glottis, and the vocal tract as a series of cylindrical tube sections with variable lengths as shown in Figure 2. The aerodynamic-acoustic simulation is based on a transmission-line circuit model of the tube system [27]. Table 1.…”

Section: Optimization Of Articulatory Targetsmentioning

confidence: 99%

Estimating vocal tract shapes of Thai vowels from contextual vowel variation

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Birkholz

2014

2014 17th Oriental Chapter of the International Committee for the Co-Ordination and Standardization of Speech Databases and Ass

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This paper presents a computational estimation of vocal tract shape parameters as articulatory targets of Thai vowels in an articulatory synthesizer, by means of analysis-bysynthesis with acoustic data as input. A speech corpus designed to capture the contextual variants of nine Thai long vowels, consisting of 81 disyllabic utterances, was recorded by a native Thai speaker. For each utterance, two targets, one for each syllable, were estimated by optimizing the target parameters to minimize the MFCC error between original and synthesized speech. An analysis-by-synthesis approach was used to iteratively optimize the shape parameters. The estimated targets of each vowel type were then averaged, resulting in nine articulatory targets, each corresponding to a vowel. The optimized targets were then used to synthesize Thai vowels both in monosyllables and in disyllabic sequences. The results indicate that the estimated targets effectively represent the underlying articulatory goals of Thai vowels.

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“…And their control of the articulator is realized via a prior arrangement of the gestural scores: target position and time constant [8], [9]. A variety of control methods have also been proposed to realize acoustic-articulatory inversion using neural networks (NNs) [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we adopt the hypothesis that the continuous speech is realized by the asymptotic approximation of the regional target positions for the categorical phonetic clusters in the articulatory space [3], [9]. We use a mass-spring based articulatory synthesizer including the lungs, the glottis and the vocal tract to simulate the myeo-elastic and aero-dynamic interactions in the vocal system.…”

Section: Introductionmentioning

confidence: 99%

An adaptive control scheme for articulatory synthesis of plosive-vowel sequences

Huang

2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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We present an adaptive control scheme in a neu ral based model to improve the performance of articulatory based speech synthesis. The model generates the articulatory trajectories for English plosive-vowel patterns through regional target approximation in the articulatory domain and categorical perception in the auditory domain. The proposed method can effectively model the variations of static vowel sounds and adapt to changes and uncertainties in the dynamic plosive sounds. Simulation studies demonstrate that the proposed scheme is able to estimate and regulate the control parameters in the articulatory synthesizer to produce smooth and authentic acoustic phonetic output. I. INT RODUCTI ONArticulatory speech synthesis holds many benefits in text to-speech (TTS) applications, e.g., automated facial animation and treatment of speech disorders [1]. Though concatenative synthesis is currently the leading method in TTS systems, it is often constrained to the available set of phonetic pat terns, speakers, and speaking styles. In contrast the articu latory synthesizer explicitly defined the phonetic properties, the physiological characteristics of the speaker, e.g., gender, age, and emotional state, and speaking style. However, it is difficult to design a articulatory synthesizer which is able to approximate the anatomic structure of the human vocal system, to generate authentic acoustic sounds, and to offer automated control of the articulators. There are few such systems that integrate the above aspects [2], [3]. Existing articulatory synthesizers usually separate the subglottal, the vocal cords and the vocal tract systems in the resulting struc ture , e.g., ArtiSynth, Vo calTractLab, et cetera [4], [5]. Some of them have to use additional acoustic module for sound generation to reduce the computational cost and to produce comparable sound output for TIS applications [6], [7]. Even though many synthesizers are able to produce sonorants such as vowels rather efficiently, the consonantal phenomena such as plosives caused by muscular constrictions are usually poorly represented. The problem also exists at phonetic boundaries, where the smooth transaction between neighboring phones requires effective control of the moving articulators.Previously Ohman used a coarticulation model to sim plify the consonantal effects as local perturbation during the vowel-consonant-vowel (VCV) synthesis. On the other hand, Birkholz, Kroger & Rube proposed to model articulatory dy namics with a tenth-order dynamical system to reproduce nat ural sounding and smooth consonant-vowel (CV) sequences.And their control of the articulator is realized via a prior arrangement of the gestural scores: target position and time constant [8], [9]. A variety of control methods have also been proposed to realize acoustic-articulatory inversion using neural networks (NNs) [10], [11]. However, when using fixed structure NNs to model the nonlinearities of the correlated the articulatory gestures and the acoustic cues, there are two major difficulties. On the ...

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Simulation of Losses Due to Turbulence in the Time-Varying Vocal System

Cited by 47 publications

References 33 publications

Identifying underlying articulatory targets of Thai vowels from acoustic data based on an analysis-by-synthesis approach

Identifying underlying articulatory targets of Thai vowels from acoustic data based on an analysis-by-synthesis approach

Estimating vocal tract shapes of Thai vowels from contextual vowel variation

An adaptive control scheme for articulatory synthesis of plosive-vowel sequences

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