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مشاهده‌ مشخصات مقاله

Combinition Of HMM and Neural Network for Phoneme Boundaries Refinement in Persian Database

Authors
  • M. M. Homayounpour
  • B. Bakhtiyari
  • M. Namnabat
Conference دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Abstract In this paper, a post-refining method using MLP neural network is proposed for phonetic segmentation and time alignment of speech databases. The main issue of the work involves the refinement of an initial estimation of phoneme boundaries. Initial phoneme boundaries are provided by a time alignment technique using Hidden Markov Model (HMM) and Multi-layer perceptron (MLP) is used to refine the initial phone boundaries. In fact, MLP neural network tries to model information in transitions between phonemes. The optimum partitioning of the entire phonetic transition space is constructed from the standpoint of minimizing the overall deviation from hand labeled positions. The experimental results show that the proposed method increases boundary estimation performance and achieves high accuracy compared to manual segmentation.
قیمت
  • برای اعضای سایت : 100,000 Rial
  • برای دانشجویان عضو انجمن : 20,000 Rial
  • برای اعضای عادی انجمن : 40,000 Rial

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