抄録
We describe a novel recurrent neural network-based language model (RNNLM) dealing with multiple time-scales of contexts. The RNNLM is now a technical standard in language model- ing because it remembers some lengths of contexts. However, the RNNLM can only deal with a single time-scale of a con- text, regardless of the subsequent words and topic of the spo- ken utterance, even though the optimal time-scale of the con- text can vary under such conditions. In contrast, our multiscale RNNLM enables incorporating with sufficient flexibility, and it makes use of various time-scales of contexts simultaneously and with proper weights for predicting the next word. Experi- mental comparisons carried out in large vocabulary spontaneous speech recognition demonstrate that introducing the multiple time-scales of contexts into the RNNLM yielded improvements over existing RNNLMs in terms of the perplexity and word er- ror rate.
本文言語 | English |
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ホスト出版物のタイトル | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
出版社 | International Speech and Communication Association |
ページ | 2366-2370 |
ページ数 | 5 |
巻 | 2015-January |
出版ステータス | Published - 2015 |
イベント | 16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany 継続期間: 2015 9月 6 → 2015 9月 10 |
Other
Other | 16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 |
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国/地域 | Germany |
City | Dresden |
Period | 15/9/6 → 15/9/10 |
ASJC Scopus subject areas
- 言語および言語学
- 人間とコンピュータの相互作用
- 信号処理
- ソフトウェア
- モデリングとシミュレーション