Speech act classification in Vietnamese Utterance and its application in smart mobile voice interaction
Thi-Lan Ngo, Quang-Vu Duong, Son Bao Pham, Xuan-Hieu Phan · 2016
We can observe the rapid development of the spoken human-machine interface, thanks to the big progress in automatic recognition and text to speech technologies. Especially, the mobile virtual assistants are becoming more popular than ever. Recognizing user's intent in interaction is one of the biggest problems while the system is designed and is attracting the attention of researchers. Identifying utterance's speech act in an automated manner is important to reveal user's intention (such as informing, asking, requesting and expressing emotion), which can provide useful indicators to improve the performance of human-machine interaction. Automatic speech act classification has been studied in many languages such as English, Chinese, Slovakian, Arabic, but not yet in Vietnamese. In this paper, we present a speech act scheme suitable for Vietnamese utterances in mobile voice inter- action. Also, we built a speech act classifier that achieved 84.36% accuracy. This classifier has been applied in our mobile virtual assistant for Vietnamese (VAV). The experiment result has demonstrated the ability of this classifier such as robust, compact and can work well on mobiles.