臺灣博碩士論文加值系統

在這篇論文，我們運用隱藏式馬可夫模型(HMM)去處理台語大辭彙語音辨識的問題。我們以聲韻母作為基本語音單位。其所得到的初步的Top1和Top5辨識率各為90.6%和97.4%。
由於傳統由左到右的隱藏式馬可夫模型缺乏對於語音單位內每一狀態停留的處理能力。因此，在過去有許多丟模擬狀態停留的方法被提出。在這篇論文，我們將合併無限狀態停留口而(Infinte duration)和有限狀態停留(Finite duratio)這兩種模型當作我們新的模擬方式。並將這種模型應用於台語大辭彙語音辨識上，它的結果將較傳統的模型方式有改進。

In this thesis, we try to use Hidden Markov Model (HMM) to solve the problem of large vocabulary recognition of Taiwanese. The phone units we used are Initial and Final units. The recognition accuracy we achieved in our initial study is 90.6% / 97.4% for Top1 / Top5 candidates respectively.
A traditional left-to-right HMM with no skipping states has been suffered from its poor ability to model the duration of each state in a speech unit. Therefore, some implicit or explicit duration models have been proposed in the literatures during the past years. In this thesis, a new hybrid duration modeling technique based on an infinite-duration HMM and a finite-duration HMM is proposed. The application to a pioneer large-vocabulary Taiwanese speech recognition task shows promising results and improvements over the traditional explicit duration modeling techniques.