Enhancing LSTM RNN-Based Speech Overlap Detection by Artificially Mixed Data

Hagerer G, Pandit V, Eyben F, Schuller B. Enhancing LSTM RNN-Based Speech Overlap Detection by Artificially Mixed Data. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017.pp. P1-1:1-8. Available from: https://aes.org/publications/elibrary-page/?id=18764

@inproceedings{Hagerer2017_18764,
  author = {Hagerer, Gerhard and Pandit, Vedhas and Eyben, Florian and Schuller, Björn},
  title = {{Enhancing LSTM RNN-Based Speech Overlap Detection by Artificially Mixed Data}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  pages = {P1-1:1-8},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18764}
}

TY  - CPAPER
TI  - Enhancing LSTM RNN-Based Speech Overlap Detection by Artificially Mixed Data
AU  - Hagerer, Gerhard
AU  - Pandit, Vedhas
AU  - Eyben, Florian
AU  - Schuller, Björn
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
SP  - 1
EP  - 8
M1  - Paper P1-1
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18764
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents a new method for Long Short-Term Memory Recurrent Neural Network (LSTM) based speech overlap detection. To this end, speech overlap data is created artificially by mixing large amounts of speech utterances. Our elaborate training strategies and presented network structures demonstrate performance surpassing the considered state-of-the-art overlap detectors. Thereby we target the full ternary task of non-speech, speech, and overlap detection. Furthermore, speakers' gender is recognised, as the first successful combination of this kind within one model.
ER  -