Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping

Laurent Girin; Thomas Hueber; Xavier Alameda-Pineda

doi:10.1109/taslp.2017.2651398

ScienceGate Book Chapters

JOURNAL ARTICLE

Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping

Laurent Girin Thomas Hueber Xavier Alameda-Pineda

Year: 2017 Journal: IEEE/ACM Transactions on Audio Speech and Language Processing Vol: 25 (3)Pages: 662-673 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/taslp.2017.2651398

Get Full-Text PDF Get Analytical Report

Abstract

International audience

Keywords:

Computer science Speech recognition Inversion (geology) Gaussian process Mixture model Gaussian Artificial intelligence Physics

Metrics

Cited By

0.69

FWCI (Field Weighted Citation Impact)

Refs

0.74

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

Speech and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Music and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping

Abstract

Metrics

Citation History

Topics

Related Documents

Speaker-Adaptive Acoustic-Articulatory Inversion Using Cascaded Gaussian Mixture Regression

Speaker adaptation of an acoustic-articulatory inversion model using cascaded Gaussian mixture regressions

Acoustic-to-articulatory inversion mapping with Gaussian mixture model

Acoustic-to-Articulatory Inversion Mapping Based on Latent Trajectory Gaussian Mixture Model

Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model