Pronunciation scoring for Indian English learners using a phone recognition system

2010 ◽  
Author(s):  
Chitralekha Bhat ◽  
K. L. Srinivas ◽  
Preeti Rao
Keyword(s):  
English Learners ◽  
Recognition System ◽  
Indian English ◽  
Phone Recognition

Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

2021 ◽  
Vol 20 (4) ◽  
pp. 1-19
Author(s):  
Manjunath K. E. ◽  
Srinivasa Raghavan K. M. ◽  
K. Sreenivasa Rao ◽  
Dinesh Babu Jayagopi ◽  
V. Ramasubramanian
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Window Size ◽  
Recognition System ◽  
Error Rates ◽  
Indian Languages ◽  
International Phonetic Alphabet ◽  
Phone Recognition ◽  
Front End ◽  
Recognition Systems

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

scholarly journals Enhancement of Speech Recognition System by neural network approaches of Clustering

2013 ◽  
Vol 6 (1) ◽  
pp. 266-271
Author(s):  
Anurag Upadhyay ◽  
Chitranjanjit Kaur
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Recurrent Neural Networks ◽  
Alternative Energy ◽  
Recognition Rate ◽  
Speech Sound ◽  
Recognition System ◽  
Training Methods ◽  
Indian Languages ◽  
Phone Recognition

This paper addresses the problem of speech recognition to identify various modes of speech data. Speaker sounds are the acoustic sounds of speech. Statistical models of speech have been widely used for speech recognition under neural networks. In paper we propose and try to justify a new model in which speech co articulation the effect of phonetic context on speech sound is modeled explicitly under a statistical framework. We study speech phone recognition by recurrent neural networks and SOUL Neural Networks. A general framework for recurrent neural networks and considerations for network training are discussed in detail. SOUL NN clustering the large vocabulary that compresses huge data sets of speech. This project also different Indian languages utter by different speakers in different modes such as aggressive, happy, sad, and angry. Many alternative energy measures and training methods are proposed and implemented. A speaker independent phone recognition rate of 82% with 25% frame error rate has been achieved on the neural data base. Neural speech recognition experiments on the NTIMIT database result in a phone recognition rate of 68% correct. The research results in this thesis are competitive with the best results reported in the literature. 

The system for relieving students’ boring emotion in English learning based on fuzzy neural network

2021 ◽  
pp. 1-10
Author(s):  
Liuhui Yang ◽  
Xiuying Wu
Keyword(s):  
Neural Network ◽  
English Learners ◽  
Fuzzy Neural Network ◽  
Theoretical Perspective ◽  
Recognition System ◽  
Algorithm Analysis ◽  
Learning Needs ◽  
English Learning ◽  
Emotional Factor ◽  
Fuzzy Neural

The ability to perceive students’ emotions in real time is related to whether the description of the student’s state is accurate, and it is also related to whether the goal of achieving the students’ individual learning needs can be achieved. This paper studies the students’ boring emotion in English learning, and builds the recognition system of students’ boring emotion in English learning based on fuzzy neural network. Moreover, this paper combines the actual needs to construct the system function modules, and carries out the algorithm analysis and framework construction, and uses the mathematical modeling method to add the emotional factor to the English learner state modeling. In addition, according to the actual needs of the system constructed in this paper, the boring emotion of English learners is recognized. In addition, this paper designs experiments to verify the performance of the model, and analyze the system reliability from the theoretical perspective and the practical perspective. The experimental research results show that the model constructed in this paper meets the expected goals.

scholarly journals Multilingual and multimode phone recognition system for Indian languages

2020 ◽  
Vol 119 ◽  
pp. 12-23
Author(s):  
Kumud Tripathi ◽  
M. Kiran Reddy ◽  
K. Sreenivasa Rao
Keyword(s):  
Recognition System ◽  
Indian Languages ◽  
Phone Recognition

Mizo Phone Recognition System

2017 ◽  
Author(s):  
Abhishek Dey ◽  
Wendy Lalhminghlui ◽  
Priyankoo Sarmah ◽  
K. Samudravijaya ◽  
S. R. Mahadeva Prasarma ◽  
...  
Keyword(s):  
Recognition System ◽  
Phone Recognition

scholarly journals Achievement, Politics, and Policy Shifts: Expert Report on Achievement for Martínez/Yazzie v. New Mexico

2019 ◽  
Vol 13 (3) ◽  
pp. 116
Author(s):  
Cristóbal Rodríguez
Keyword(s):  
New Mexico ◽  
American Indian ◽  
English Learners ◽  
Educational Opportunity ◽  
The State ◽  
Bilingual Students ◽  
Indian English ◽  
Expert Report ◽  
American Indian Students ◽  
Achievement Levels

The achievement levels from 2007 to 2016 in the state of New Mexico demonstrate an educational system that is failing its Hispanic, American Indian, and English learner students. During this period of time, close to 30% of Hispanic students were proficient or above in reading, math, and science, and close to 25% of American Indian students were proficient or above. Moreover, a change in politics that informed changes in curriculum and testing policies during this period of time show lowering proficiency rates and grater disparities between groups. Further and more problematically for a state that is historically bilingual, and as bilingual students tend to be Hispanic and American Indian, English learners in most recent years tested at the lowest levels of proficiency and above. These sobering achievement levels highlighted in this article were used as evidence and as testimony in the expert report in the conjoined educational opportunity cases Martínez v. New Mexico (2019) and Yazzie v. New Mexico (2019), which was a case filed on behalf of underrepresented families and students in New Mexico against the state’s Public Education Department. The result was a landmark decision that decided children in New Mexico indeed have a right to an education and mandated the state to respond immediately to these disparities. Herein are the findings and conclusions from the expert report and testimony from the Martínez v. New Mexico (2019) and Yazzie v. New Mexico (2019) Trial Declaration of Cristobal Rodriguez.

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