Automatic speaker verification from affective speech using Gaussian mixture model based estimation of neutral speech characteristics

Author:

Avila Anderson R.,O’Shaughnessy Douglas,Falk Tiago H.

Funder

Natural Sciences and Engineering Research Council of Canada

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fonds de recherche du Québec – Nature et technologies

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modelling and Simulation,Software

Reference59 articles.

1. Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers;Akcay;Speech Commun.,2020

2. Channel-robust speaker identification using modified-mean cepstral mean normalization with frequency warping;Alvin,1999

3. The effect of speech rate on automati speaker verification: a comparative analysis of GMM-ubm and i-vector based methods;Avila,2014

4. Investigating the use of modulation spectral features within an i-vector framework for far-field automatic speaker verification;Avila,2014

5. Feature pooling of modulation spectrum features for improved speech emotion recognition in the wild;Avila;IEEE Trans. Affect. Comput.,2018

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