Name
Title | ||
|---|---|---|
Self-Attention Based Model For Punctuation Prediction Using Word And Speech Embeddings |
Abstract | ||
|---|---|---|
This paper proposes to use self-attention based model to predict punctuation marks for word sequences. The model is trained using word and speech embedding features which are obtained from the pre-trainedWord2Vec and Speech2Vec, respectively. Thus, the model can use any kind of textual data and speech data. Experiments are conducted on English IWSLT2011 datasets. The results show that the self-attention based model trained using word and speech embedding features outperforms the previous state-of-the-art single model by up to 7.8% absolute overall F-1-score. The results also show that it obtains performance improvement by up to 4.7% absolute overall F-1-score against the previous best ensemble model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682260 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Self-attention, transfer learning, word embedding, speech embedding, punctuation prediction | Embedding,Ensemble forecasting,Pattern recognition,Task analysis,Computer science,Speech recognition,Artificial intelligence,Word2vec,Artificial neural network,Hidden Markov model,Punctuation,Encoding (memory) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangyan Yi | 1 | 19 | 17.99 |
| Jianhua Tao | 2 | 848 | 138.00 |