Automatic Pronunciation Scoring with Score Combination by Learning to Rank and Class-Normalized DP-based Quantization
博士 === 國立清華大學 === 資訊系統與應用研究所 === 103 === This thesis describes an automatic pronunciation scoring framework using learning to rank and class-normalized, dynamic-programming-based quantization. The goal is to train a model that is able to grade the pronunciation of a second language learner, such tha...
Main Authors: | Chen, Liang-Yu, 陳亮宇 |
---|---|
Other Authors: | Jyh-Shing Roger Jang |
Format: | Others |
Language: | zh-TW |
Published: |
2015
|
Online Access: | http://ndltd.ncl.edu.tw/handle/24788225549072910792 |
Similar Items
-
Combining Multiple Acoustic Models to Improve English Pronunciation Scoring
by: Chen, Yang-Shen, et al.
Published: (2011) -
Improving Taiwanese Pronunciation Scoring via Multiple Acoustic Models
by: Chen, Hung-Jui, et al.
Published: (2011) -
Inheritance of Properties of Normal and Non-Normal Distributions After Transformation of Scores to Ranks
by: Donald W. Zimmerman
Published: (2011-01-01) -
Pronunciation learning for automatic speech recognition
by: Badr, Ibrahim
Published: (2011) -
Ranking DMUs by Combining Cross-Efficiency Scores Based on Shannon’s Entropy
by: Yueh-Chiang Lee
Published: (2019-05-01)