| Paper ID | HLT-6.4 |
| Paper Title |
RNN TRANSDUCER MODELS FOR SPOKEN LANGUAGE UNDERSTANDING |
| Authors |
Samuel Thomas, Hong-Kwang Kuo, George Saon, Zoltan Tuske, Brian Kingsbury, Gakuto Kurata, Zvi Kons, Ron Hoory, IBM Research AI, United States |
| Session | HLT-6: Language Understanding 2: End-to-end Speech Understanding 2 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding (SLU). These end-to-end (E2E) models are constructed in three practical settings: a case where verbatim transcripts are available, a constrained setting where the only available annotations are SLU labels and their values, and more a restrictive case where transcripts are available but not corresponding audio. We show how RNN-T SLU models can be developed starting from pre-trained automatic speech recognition (ASR) systems, followed by an SLU adaptation step. In settings where real audio data is not available, artificially synthesized speech is used to successfully adapt various SLU models. When evaluated on two SLU data sets, the ATIS corpus and a customer call center data set, the proposed models closely track the performance of other E2E models and achieve state-of-the-art results. |
