| Presentation | 2022-11-30 Dialogue disfluency detection using context Hiroto Nakashima, Kazutaka Shimada, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Recent automatic speech recognition (ASR) techniques have been improved by a large amount of training data and machine learning, such as deep learning technology. Problems in the outputs from ASR are not only recognition errors but also outputs caused by disfluency from speakers. It is difficult to remove them automatically, and removing them by hand is costly. In this paper, we propose a disfluency detection model with BERT. The model utilizes context information of target utterances. We introduce two types of context information. The first one is real utterances that appear around the target utterance. We compare several sequence lengths of the previous and following utterances. The second one is a generated utterance by GPT-2. Our model adds the utterance generated from the target utterance as the following context. In the experiment, the long sequence improves the disfluency detection accuracy, and real context outperforms generated context. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | disfluency detection / disfluency / dialogue generation / context complement |
| Paper # | NLC2022-13,SP2022-33 |
| Date of Issue | 2022-11-22 (NLC, SP) |
| Conference Information | |
| Committee | NLC / IPSJ-NL / SP / IPSJ-SLP |
|---|---|
| Conference Date | 2022/11/29(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Mitsuo Yoshida(Univ. of Tsukuba) / 須藤 克仁(奈良先端科学技術大学院大学) / Tomoki Toda(Nagoya Univ.) / 戸田 智基(名古屋大学) |
| Vice Chair | Hiroki Sakaji(Univ. of Tokyo) / Takeshi Kobayakawa(NHK) |
| Secretary | Hiroki Sakaji(NTT) / Takeshi Kobayakawa(Hiroshima Univ. of Economics) / (株式会社デンソーアイティーラボラトリ) / (北海学園大学) / (東京農工大学) |
| Assistant | Kanjin Takahashi(Sansan) / Yasuhiro Ogawa(Nagoya Univ.) / / Ryo Aihara(Mitsubishi Electric) / Daisuke Saito(Univ. of Tokyo) |
| Paper Information | |
| Registration To | Technical Committee on Natural Language Understanding and Models of Communication / Special Interest Group on Natural Language / Technical Committee on Speech / Special Interest Group on Spoken Language Processing |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Dialogue disfluency detection using context |
| Sub Title (in English) | |
| Keyword(1) | disfluency detection |
| Keyword(2) | disfluency |
| Keyword(3) | dialogue generation |
| Keyword(4) | context complement |
| 1st Author's Name | Hiroto Nakashima |
| 1st Author's Affiliation | Kyusyu Institute of Technology(KIT) |
| 2nd Author's Name | Kazutaka Shimada |
| 2nd Author's Affiliation | Kyusyu Institute of Technology(KIT) |
| Date | 2022-11-30 |
| Paper # | NLC2022-13,SP2022-33 |
| Volume (vol) | vol.122 |
| Number (no) | NLC-287,SP-288 |
| Page | pp.pp.21-26(NLC), pp.21-26(SP), |
| #Pages | 6 |
| Date of Issue | 2022-11-22 (NLC, SP) |