Exploring End-to-End Techniques for Low-Resource Speech Recognition
2018
In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 h). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization techniques are compared as well. We also proposed CTC-loss modification using segmentation during training, which leads to improvement while decoding with small beam size.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
21
References
8
Citations
NaN
KQI