The Effect of Speaking Style on a Locus Equation Characterization of Stop Place of Articulation

Sussman, Harvey M.; Dalston, Eileen T.; Gumbert, Sam D.

doi:10.1159/000028433

Cited by 18 publications

(25 citation statements)

References 34 publications

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“…In speech, several studies have looked for invariants, either in the extrinsic acoustic domain (Stevens & Blumstein, 1978;Strange et al, 1983;Sussman et al, 1998), or in the intrinsic domain (Fujimura, 1986;Boë et al, 1992;Tremblay et al, 2003;Baer et al, 1988;Maeda & Honda, 1994). Analogously to what force-fields do for limb movement, perturbation paradigms have been elaborated to break down the usual associations between extrinsic (acoustics) and intrinsic (articulation) variables.…”

Section: Extrinsic Versus Intrinsic Motor Goals In Speech Productionmentioning

confidence: 99%

Limb versus Speech Motor Control: A Conceptual Review

Britta¹,

Fuchs²,

Perrier³

et al. 2011

Motor Control

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This paper presents a comparative conceptual review of speech and limb motor control.Speech is essentially cognitive in nature and constrained by the rules of language, while limb movement is often oriented to physical objects. We discuss the issue of intrinsic vs. extrinsic variables underlying the representations of motor goals as well as whether motor goals specify terminal postures or entire trajectories. Timing and coordination is recognized as an area of strong interchange between the two domains. Although coordination among different motor acts within a sequence and co-articulation are central to speech motor control, they have received only limited attention in manipulatory movements. The biomechanics of speech production is characterized by the presence of soft tissue, a variable number of degrees of freedom, and the challenges of high rates of production, while limb movements deal more typically with inertial constraints from manipulated objects. This comparative review thus leads us to identify many strands of thinking that are shared across the two domains, but also points us to issues on which approaches in the two domains differ. We conclude that conceptual interchange between the fields of limb and speech motor control has been useful in the past and promises continued benefit.

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Section: Extrinsic Versus Intrinsic Motor Goals In Speech Productionmentioning

confidence: 99%

Limb versus Speech Motor Control: A Conceptual Review

Britta¹,

Fuchs²,

Perrier³

et al. 2011

Motor Control

View full text Add to dashboard Cite

show abstract

“…For example, slopes for the bilabial plosives such as /b/ are consistently steeper and corresponding y-intercepts, lower, compared to those observed for the alveolar cognate /d/ (e.g. Fowler 1994, McLeod et al 2001, Sussman, Bessell, Dalston & Majors 1997, Sussman, Dalston & Gumbert 1998. These observations suggest that /b/ in CV sequences has the highest levels of coarticulation with the ensuing vowel, and therefore the lowest levels of articulatory resistance due to the independence between the main articulators involved in the production of /bV/; the lips and the lingual system.…”

Section: Temporal and Spectro-temporal Measures Of Speechmentioning

confidence: 93%

Speaker sex effects on temporal and spectro-temporal measures of speech

Herrmann¹,

Cunningham²,

Whiteside³

2014

Journal of the International Phonetic Association

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This study investigated speaker sex differences in the temporal and spectro-temporal parameters of English monosyllabic words spoken by thirteen women and eleven men. Vowel and utterance duration were investigated. A number of formant frequency parameters were also analysed to assess the spectro-temporal dynamic structures of the monosyllabic words as a function of speaker sex. Absolute frequency changes were measured for the first (F1), second (F2) and third (F3) formant frequencies (ΔF1, ΔF2, and ΔF3, respectively). Rates of these absolute formant frequency changes were also measured and calculated to yield measurements for rF1, rF2 and rF3. Normalised frequency changes (normΔF1, normΔF2, and normΔF3), and normalised rates of change (normrF1, normrF2, and normrF3) were also calculated. F2 locus equations were then derived from the F2 measurements taken at the onset and temporal mid points of the vowels.Results indicated that there were significant sex differences in the spectro-temporal parameters associated with F2: ΔF2, normΔF2, rF2, and F2 locus equation slopes; women displayed significantly higher values for ΔF2, normΔF2 and rF2, and significantly shallower F2 locus equation slopes. Collectively, these results suggested lower levels of coarticulation in the speech samples of the women speakers, and corroborates evidence reported in earlier studies.

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“…For a particular phonetic class c, the joint probabilities of the phonetic-class dependent UBMs are determined by: (8) where c ∈ {1, . .…”

Section: B Phonetic-class Dependent Ubmsmentioning

confidence: 99%

“…For example, studies in speech prosody have shown that individual speakers exhibit substantial differences in voluntary speaking behaviors such as lexicon, prosody, intonation, pitch range, and pronunciation [6], [7]. Studies in linguistics have shown that speaking styles have significant effect on pronunciation patterns [8]. Kuehn and Moll [9] measured the velocity and displacement of the tongue during speech production and found appreciable variation of these two measurements among different speakers.…”

Section: Introductionmentioning

confidence: 99%

Speaker Verification via High-Level Feature Based Phonetic-Class Pronunciation Modeling

Zhang

Mak

Meng

2007

IEEE Trans. Comput.

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Abstract-It has been shown recently that the pronunciation characteristics of speakers can be represented by articulatory feature-based conditional pronunciation models (AFCPMs). However, the pronunciation models are phoneme-dependent, which may lead to speaker models with low discriminative power when the amount of enrollment data is limited. This paper proposes to mitigate this problem by grouping similar phonemes into phonetic classes and representing background and speaker models as phonetic-class dependent density functions. Phonemes are grouped by (1) vector quantizing the discrete densities in the phoneme-dependent universal background models, (2) using the phone properties specified in the classical phoneme tree, or (3) combining vector quantization and phone properties. Evaluations based on 2000 NIST SRE show that this phonetic-class approach effectively alleviates the data spareness problem encountered in conventional AFCPM, which results in better performance when fused with acoustic features.

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The Effect of Speaking Style on a Locus Equation Characterization of Stop Place of Articulation

Cited by 18 publications

References 34 publications

Limb versus Speech Motor Control: A Conceptual Review

Limb versus Speech Motor Control: A Conceptual Review

Speaker sex effects on temporal and spectro-temporal measures of speech

Speaker Verification via High-Level Feature Based Phonetic-Class Pronunciation Modeling

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