مشاهده مشخصات مقاله
Combinition Of HMM and Neural Network for Phoneme Boundaries Refinement in Persian Database
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نویسنده (ها) |
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M. M. Homayounpour
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B. Bakhtiyari
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M. Namnabat
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مربوط به کنفرانس |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
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. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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خرید مقاله
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