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Journal articles on the topic 'Acoustic-Articulatory Mapping'

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1

Zussa, F., Q. Lin, G. Richard, D. Sinder, and J. Flanagan. "Open‐loop acoustic‐to‐articulatory mapping." Journal of the Acoustical Society of America 98, no. 5 (1995): 2931. http://dx.doi.org/10.1121/1.414151.

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2

Riegelsberger, Edward L., and Ashok K. Krishnamurthy. "Acoustic‐to‐articulatory mapping of fricatives." Journal of the Acoustical Society of America 97, no. 5 (1995): 3417. http://dx.doi.org/10.1121/1.412480.

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3

Ananthakrishnan, G., and Olov Engwall. "Mapping between acoustic and articulatory gestures." Speech Communication 53, no. 4 (2011): 567–89. http://dx.doi.org/10.1016/j.specom.2011.01.009.

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4

Sepulveda-Sepulveda, Alexander, and German Castellanos-Domínguez. "Time-Frequency Energy Features for Articulator Position Inference on Stop Consonants." Ingeniería y Ciencia 8, no. 16 (2012): 37–56. http://dx.doi.org/10.17230/ingciencia.8.16.2.

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Acoustic-to-Articulatory inversion offers new perspectives and interesting applicationsin the speech processing field; however, it remains an open issue. This paper presents a method to estimate the distribution of the articulatory informationcontained in the stop consonants’ acoustics, whose parametrizationis achieved by using the wavelet packet transform. The main focus is on measuringthe relevant acoustic information, in terms of statistical association, forthe inference of the position of critical articulators involved in stop consonantsproduction. The rank correlation Kendall coefficient
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5

Sorokin, V. N., and A. V. Trushkin. "Articulatory-to-acoustic mapping for inverse problem." Speech Communication 19, no. 2 (1996): 105–18. http://dx.doi.org/10.1016/0167-6393(96)00028-3.

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6

Wu, Zhiyong, Kai Zhao, Xixin Wu, Xinyu Lan, and Helen Meng. "Acoustic to articulatory mapping with deep neural network." Multimedia Tools and Applications 74, no. 22 (2014): 9889–907. http://dx.doi.org/10.1007/s11042-014-2183-z.

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7

Riegelsberger, Edward L., and Ashok K. Krishnamurthy. "Acoustic‐to‐articulatory mapping of isolated and intervocalic fricatives." Journal of the Acoustical Society of America 101, no. 5 (1997): 3175. http://dx.doi.org/10.1121/1.419149.

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8

Atal, Bishnu. "A study of ambiguities in the acoustic-articulatory mapping." Journal of the Acoustical Society of America 122, no. 5 (2007): 3079. http://dx.doi.org/10.1121/1.2942998.

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9

McGowan, Richard S., and Michael A. Berger. "Acoustic-articulatory mapping in vowels by locally weighted regression." Journal of the Acoustical Society of America 126, no. 4 (2009): 2011. http://dx.doi.org/10.1121/1.3184581.

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10

Schmidt, Anna Marie. "Korean to English articulatory mapping: Palatometric and acoustic data." Journal of the Acoustical Society of America 95, no. 5 (1994): 2820–21. http://dx.doi.org/10.1121/1.409681.

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11

Shahrebabaki, Abdolreza Sabzi, Giampiero Salvi, Torbjorn Svendsen, and Sabato Marco Siniscalchi. "Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models." IEEE/ACM Transactions on Audio, Speech, and Language Processing 30 (2022): 135–47. http://dx.doi.org/10.1109/taslp.2021.3133218.

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12

Chennoukh, S., D. Sinder, G. Richard, and J. Flanagan. "Methods for acoustic‐to‐articulatory mapping and voice mimic systems." Journal of the Acoustical Society of America 101, no. 5 (1997): 3179. http://dx.doi.org/10.1121/1.419218.

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13

Behbood, Hossein, Seyyed Ali Seyyedsalehi, Hamid Reza Tohidypour, Mojtaba Najafi, and Shahriar Gharibzadeh. "A novel neural-based model for acoustic-articulatory inversion mapping." Neural Computing and Applications 21, no. 5 (2011): 935–43. http://dx.doi.org/10.1007/s00521-011-0563-0.

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14

You, Kang, Kele Xu, Jilong Wang, and Ming Feng. "Domain adaptation towards speaker-independent ultrasound tongue imaging based articulatory-to-acoustic conversion." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A366. http://dx.doi.org/10.1121/10.0019181.

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In this paper, we endeavor to address an articulatory-to-acoustic issue which aims to estimate the mel-spectrogram of the acoustical signals, using midsagittal ultrasound tongue images of the vocal tract as input. Previous attempts employed statistical methods for the inversion between the articulatory movements and speech, while deep learning has begun to dominate this field. Despite the sustainable efforts that have been made, the mapping performance can be greatly varied for different speakers and most of the previous methods are constrained for the speaker-dependent scenario. Here, we pres
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15

Kello, Christopher T., and David C. Plaut. "A neural network model of the articulatory-acoustic forward mapping trained on recordings of articulatory parameters." Journal of the Acoustical Society of America 116, no. 4 (2004): 2354–64. http://dx.doi.org/10.1121/1.1715112.

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16

Honda, Masaaki, and Tokihiko Kaburagi. "Estimation of articulatory‐to‐acoustic mapping using input and output measurements." Journal of the Acoustical Society of America 93, no. 4 (1993): 2353–54. http://dx.doi.org/10.1121/1.406212.

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17

Dusan, Sorin, and Li Deng. "Vocal‐tract length normalization for acoustic‐to‐articulatory mapping using neural networks." Journal of the Acoustical Society of America 106, no. 4 (1999): 2181. http://dx.doi.org/10.1121/1.427279.

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18

Girin, Laurent, Thomas Hueber, and Xavier Alameda-Pineda. "Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 3 (2017): 662–73. http://dx.doi.org/10.1109/taslp.2017.2651398.

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19

Toda, Tomoki, Alan W. Black, and Keiichi Tokuda. "Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model." Speech Communication 50, no. 3 (2008): 215–27. http://dx.doi.org/10.1016/j.specom.2007.09.001.

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20

Fonseca, Marco. "Japanese devoiced vowel tongue movement: Acoustics and articulory mapping." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A372. http://dx.doi.org/10.1121/10.0019215.

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The goal of this study is to evaluate the tongue movements of Japanese devoiced vowels /i/ and /u/ using acoustic and articulatory data. Four speakers of Japanese were recorded with an EchoBlaster 128Z ultrasound probe attached to their chin. Participants read a total of 22 tokens in a carrier sentence (12 repetitions). Duration (ms) and Center of Gravity (CoG, Hz) was fit in two linear mixed effects models as dependent variables, and the interaction between voicing (voiced/devoiced) and vowel (i/u) as predictors. For the duration values, there was an effect of both predictors. There was an ef
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21

Csapó, Tamás Gábor, Gábor Gosztolya, László Tóth, Amin Honarmandi Shandiz, and Alexandra Markó. "Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping." Sensors 22, no. 22 (2022): 8601. http://dx.doi.org/10.3390/s22228601.

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Within speech processing, articulatory-to-acoustic mapping (AAM) methods can apply ultrasound tongue imaging (UTI) as an input. (Micro)convex transducers are mostly used, which provide a wedge-shape visual image. However, this process is optimized for the visual inspection of the human eye, and the signal is often post-processed by the equipment. With newer ultrasound equipment, now it is possible to gain access to the raw scanline data (i.e., ultrasound echo return) without any internal post-processing. In this study, we compared the raw scanline representation with the wedge-shaped processed
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22

Howard, Ian S., and Mark A. Huckvale. "Learning to control an articulatory synthesizer by imitating real speech." ZAS Papers in Linguistics 40 (January 1, 2005): 63–78. http://dx.doi.org/10.21248/zaspil.40.2005.258.

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The goal of our current project is to build a system that can learn to imitate a version of a spoken utterance using an articulatory speech synthesiser. The approach is informed and inspired by knowledge of early infant speech development. Thus we expect our system to reproduce and exploit the utility of infant behaviours such as listening, vocal play, babbling and word imitation. We expect our system to develop a relationship between the sound-making capabilities of its vocal tract and the phonetic/phonological structure of imitated utterances. At the heart of our approach is the learning of
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23

EDGERTON, MICHAEL EDWARD. "Palatal Sound: a comprehensive model of vocal tract articulation*." Organised Sound 4, no. 2 (1999): 93–110. http://dx.doi.org/10.1017/s1355771899002058.

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Palatal Sound is a model of vocal tract articulation influenced by physiologic and acoustic analysis of the voice. Specifically, the term articulation refers to all movement within the vocal tract that results in open, filter-like sonorities, as well as in turbulent to absolute airflow modification. This model presents a complete mapping of place within the vocal tract that features flexibility across different vocal tract sizes and proportions. The principles behind this comprehensive mapping of acoustic and physical sound production techniques should not be foreign to those persons who creat
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24

Alonso Arteche, M. Fernanda, Nicola Phillips, Samin Moradi, et al. "Exploring infant talker bias: Insights from remote speech perception testing." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A170. http://dx.doi.org/10.1121/10.0027203.

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Lab studies show that infants (4- to 7-month-olds) prefer to listen to vowels with infant-like f0 and formant frequencies over those of an adult female (Masapollo et al., 2015; Polka et al., 2021). This Infant Talker Bias may facilitate infants’ mapping of articulatory gestures to acoustic correlates. In this study, 4- to 12-month-olds completed a listening preference task on the Lookit online testing platform. Across eight trials, we presented synthesized infant and adult vowel sounds (/i/ and /a/) paired with a simple animation and recorded the infant’s response via the webcam. Infant lookin
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25

Callan, Daniel E., Ray D. Kent, Frank H. Guenther, and Houri K. Vorperian. "An Auditory-Feedback-Based Neural Network Model of Speech Production That Is Robust to Developmental Changes in the Size and Shape of the Articulatory System." Journal of Speech, Language, and Hearing Research 43, no. 3 (2000): 721–36. http://dx.doi.org/10.1044/jslhr.4303.721.

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The purpose of this article is to demonstrate that self-produced auditory feedback is sufficient to train a mapping between auditory target space and articulator space under conditions in which the structures of speech production are undergoing considerable developmental restructuring. One challenge for competing theories that propose invariant constriction targets is that it is unclear what teaching signal could specify constriction location and degree so that a mapping between constriction target space and articulator space can be learned. It is predicted that a model trained by auditory fee
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26

Berry, Jeffrey J. "Kinematic redundancies in /u/ production with and without dysarthria." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A244. http://dx.doi.org/10.1121/10.0023427.

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The speech sensorimotor control system exploits kinematic redundancies: various articulatory degrees of freedom can exhibit trading relationships in service of a speech goal. These redundancies affect movements within and across articulators, resulting in multiple “motor equivalent” patterns that may lead to perceptually equivalent phonemes. One classic example of this redundancy is the purported trading relationship between the tongue and lips in controlling the frequency of the second formant (F2). For the acoustic goal of achieving a relatively low F2 frequency in the vowel /u/, neurotypica
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27

Ananthakrishnan, G., Olov Engwall, and Daniel Neiberg. "Exploring the Predictability of Non-Unique Acoustic-to-Articulatory Mappings." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 10 (2012): 2672–82. http://dx.doi.org/10.1109/tasl.2012.2210876.

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28

Georges, Marc-Antoine, Marvin Lavechin, Jean-Luc Schwartz, and Thomas Hueber. "Decode, move and speak! Self-supervised learning of speech units, gestures, and sounds relationships using vocal imitation." Computational Linguistics, July 22, 2024, 1–28. http://dx.doi.org/10.1162/coli_a_00532.

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Abstract Speech learning encompasses mastering a complex motor system to produce speech sounds from articulatory gestures while simultaneously uncovering discrete units that provide entry to the linguistic system. Remarkably, children acquire these associations between speech sounds, articulatory gestures, and linguistic units in a weakly supervised manner, without the need for explicit labeling of auditory inputs or access to target articulatory gestures. This study uses self-supervised deep learning to investigate the respective roles of sounds, gestures, and linguistic units in speech acqui
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29

Sepúlveda-Sepúlveda, Franklin Alexander, César Germán Castellanos-Domínguez, and Pedro Gómez-Vilda. "Subject-independent acoustic-to-articulatory mapping of fricative sounds by using vocal tract length normalization." Revista Facultad de Ingeniería Universidad de Antioquia, no. 77 (December 2015). http://dx.doi.org/10.17533/udea.redin.n77a19.

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30

Pathi, Soujanya, and Prakash Mondal. "The mental representation of sounds in speech sound disorders." Humanities and Social Sciences Communications 8, no. 1 (2021). http://dx.doi.org/10.1057/s41599-021-00706-z.

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AbstractThe objective of this study is to investigate facets of the human phonological system in an attempt to elucidate the special nature of mental representations and operations underlying some of the errors in speech sound disorders (SSDs). After examining different theories on the mental representations of sounds and their organization in SSDs, we arrive at the conclusion that the existing elucidations on the phonological representations do not suffice to explain some distinctive facets of SSDs. Here, we endorse a hypothesis in favor of representationalism but offer an alternative concept
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