A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning
| Autor: | Romain Laroche, Fabrice Lefèvre, Hatim Khouzaimi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Service (systems architecture)
Computer science business.industry Turn-taking 02 engineering and technology Theoretical Computer Science Human-Computer Interaction 030507 speech-language pathology & audiology 03 medical and health sciences Order (business) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Artificial intelligence Architecture 0305 other medical science business Software |
| Zdroj: | Computer Speech & Language. 47:93-111 |
| ISSN: | 0885-2308 |
| DOI: | 10.1016/j.csl.2017.07.006 |
| Popis: | This article introduces a new methodology to enhance an existing traditional Spoken Dialogue System (SDS) with optimal turn-taking capabilities in order to increase dialogue efficiency. A new approach for transforming the traditional dialogue architecture into an incremental one at a low cost is presented: a new turn-taking decision module called the Scheduler is inserted between the Client and the Service. It is responsible for handling turn-taking decisions. Then, a User Simulator which is able to interact with the system using this new architecture has been implemented and used to train a new Reinforcement Learning turn-taking strategy. Compared to a non-incremental and a handcrafted incremental baselines, it is shown to perform better in simulation and in a real live experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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