Projects per year
In this paper we propose speaker characterization using time delay neural networks and long short-term memory neural networks (TDNN-LSTM) speaker embedding. Three types of front-end feature extraction are investigated to find good features for speaker embedding. Three kinds of data augmentation are used to increase the amount and diversity of the training data. The proposed methods are evaluated with the National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) tasks. Experimental results show that the proposed methods achieve a decision cost of 0.400 with the pooled SRE 2018 development set with a single system. In addition, by applying simple average score combination on the outputs of 12 systems, the proposed methods achieve an equal error rate (EER) of 5.56% and a minimum decision cost function of 0.423 with the SRE 2016 evaluation set.
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2019|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: 12 May 2019 → 17 May 2019
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||12/05/19 → 17/05/19|
- NIST SRE2018
- speaker embedding
FingerprintDive into the research topics of 'Speaker Characterization Using TDNN-LSTM Based Speaker Embedding'. Together they form a unique fingerprint.
- 1 Finished