Speaker Identification for Swiss German with Spectral and Rhythm Features

Lykartsis A, Weinzierl S, Dellwo V. Speaker Identification for Swiss German with Spectral and Rhythm Features. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017. Paper 2-1. Available from: https://aes.org/publications/elibrary-page/?id=18753

@inproceedings{Lykartsis2017_18753,
  author = {Lykartsis, Athanasios and Weinzierl, Stefan and Dellwo, Volker},
  title = {{Speaker Identification for Swiss German with Spectral and Rhythm Features}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  note = {Paper 2-1},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18753}
}

TY  - CPAPER
TI  - Speaker Identification for Swiss German with Spectral and Rhythm Features
AU  - Lykartsis, Athanasios
AU  - Weinzierl, Stefan
AU  - Dellwo, Volker
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
M1  - Paper 2-1
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18753
PB  - Audio Engineering Society
LA  - en
AB  - We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity.
ER  -