Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new tr

Kaldiツールキットを用いた 音声認識システムの構築 篠崎隆宏 東京工業大学 工学院 情報通信系 www.ts.ip.titech.ac.jp 1 2016年10月27日SP研究会 • 音声認識システムの仕組み • Kaldiツールキットの概要 • 日本語話し言葉音声認識のためのKaldi用CSJレシピ • デモ用CSJレシピを用いたチュートリアル 2 概要 音声認識システムの仕組み 3 音声認識システムの全体構成 4 A/D変換 特徴量抽出 デコーディング Hello! マイク 計算機 特徴量抽出 • 認識性能の向上 • 認識のための計算量やメモリ量の削減 5 特徴量 抽出 Time 音声信号 特徴量系列 認識に有用な特徴を認識処理に都合の良い形で抽出 音声ベクトルの切り出し 6 窓(フレーム) 幅 フレームシフト メル周波数ケプストラム(MFC) 7 Mel‐Scale Filter

Authors Ilya Sutskever, Oriol Vinyals, Quoc V. Le Abstract Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Why Caffe? Expressive architecture encourages application and innovat