Pronunciation Error Detection Method base a Decision Tr
نویسندگان
چکیده
We are developing a CALL system to train English pronunciation for Japanese native speakers. However, the precision of the error detection was not very high because the threshold for the detection was not optimum. To improve the detection accuracy, we propose a new method to optimize the thresholds of error detection. The proposed method makes several clusters of the pronunciation error rules, and the thresholds are determined for each cluster. An experiment was carried out to investigate the performance of the proposed method. As a result, about 90% of detection rate was obtained, which is a remarkable improvement from the conventional method.
منابع مشابه
Automatic pronunciation error detection: an acoustic-phonetic approach
In this paper, we present an acoustic-phonetic approach to automatic pronunciation error detection. Classifiers using techniques such as Linear Discriminant Analysis or a decision tree were developed for three sounds that are frequently pronounced incorrectly by L2-learners of Dutch: /A/, /Y/ and /x/. The acoustic properties of these pronunciation errors were examined so as to define a number o...
متن کاملAutomatic detection of frequent pro L2-learne
In this paper, we present an acoustic-phonetic approach to automatic pronunciation error detection. Classifiers using techniques such as Linear Discriminant Analysis and Decision Trees were developed for three sounds that are frequently pronounced incorrectly by L2-learners of Dutch: / /, / / and / /. This paper will focus mainly on the problems with the latter phoneme. The acoustic properties ...
متن کاملPronunciation error detection techniques for children's speech
In this article we present a novel method for automatic pronunciation error detection of children’s speech. A phone graph is generated from the audio segment and augmented if necessary with alignments of phonetic transcriptions of the word to score. This graph is used for extracting phone-level features using conventional HMM/GMM acoustic scores and Support Vector Machine (SVM) classifiers acti...
متن کاملAutomatic Pronunciation Error Detection Based on Extended Pronunciation Space Using the Unsupervised Clustering of Pronunciation Errors
Calculating posterior probability within a standard pronunciation space (SPS) is a common method in automatic pronunciation error detection (APED). However, to pronunciation errors outside the SPS, this kind of methods can only give an approximate solution, that may be not right in many applications. This paper expands the SPS to include more pronunciation errors, introduces a Bhattacharyya dis...
متن کاملArticulatory Modeling for Pronunciation Error Detection without Non-Native Training Data Based on DNN Transfer Learning
Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without such data, which is difficult to c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005