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Naomitsu Ikeda

dblp:87/4937 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Speech recognition and synthesis · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis › speech analysis
acoustic feature analysis
0.512021
Vocal Tract Length Estimation Using Accumulated Means of Formants and Its Effects on Speaker-Normalization · IEEE ACM Trans. Audio Speech Lang. Process. 2021
Natural language and speech › Speech recognition and synthesis › speech analysis
formant tracking
0.512021
Vocal Tract Length Estimation Using Accumulated Means of Formants and Its Effects on Speaker-Normalization · IEEE ACM Trans. Audio Speech Lang. Process. 2021
Natural language and speech › Speech recognition and synthesis
speaker normalization
0.512021
Vocal Tract Length Estimation Using Accumulated Means of Formants and Its Effects on Speaker-Normalization · IEEE ACM Trans. Audio Speech Lang. Process. 2021

Methods — techniques the papers use, named apart from their topics

inverse-filter control · 0.5formant trajectory analysis · 0.5
YearPublicationVenuePosition
2021 Vocal Tract Length Estimation Using Accumulated Means of Formants and Its Effects on Speaker-Normalization
abstract
Differences in vocal tract lengths (VTLs) in individual speakers cause variations in acoustic features of phonemes. In this paper, a simple method to estimate speaker-specific VTLs and to quantitatively evaluate some speaker-normalization effects of the VTLs is proposed. We employed accumulated means of formant trajectories to estimate the VTLs of speakers ranging from children to adults. For the formant estimation, the inverse-filter control (IFC) system was used. In the system, the decision of analysis order, which means number of formants to be estimated, is automated. Moreover, to evaluate the speaker-normalization effect of VTLs, we proposed the data reduction method, which can reasonably find dense areas of ellipses from distributions in the formant space. Using these ellipse areas, we evaluated the three normalization effects of VTLs: normalization by the mean of all VTLs as the standard, by speaker-categorical means of VTLs, and by individual VTLs. The area reduced from the standard area of the original data by 39.5% and 46.6% in the case of the categorical means and individual VTLs, respectively. As a result, our proposed method was used to provide a “normalized vowel map (NVM)” that visualizes universal vowel-distributions as a core image of linguistic information. Finally, we compared the estimated VTLs with those by another method based on magnetic resonance imaging (MRI) data, using the proposed methods.
Tadashi Sakata, Naomitsu Ikeda, Yuichi Ueda, Akira Watanabe
IEEE ACM Trans. Audio Speech Lang. Process.2