Automatic Pronunciation Scoring And Mispronunciation Detection Using CMUSphinx

Contextually Based Data Derived Pronunciation Networks for Automatic Speech Recognition C o n t e x t u a l l y B a s e d D a t a D e r i v e d P r o n u n c i a t i o n N e t w o r k s

The bigram phoneme LM of each phone recognizer is trained on the phonemes obtained from forced aligning the training transcript to the speech data using that recognizer’s

In this paper we compare the per- formance of an automated speech scoring en- gine using two corpora: a corpus of almost 700,000 randomly sampled spoken responses with scores

In [9] it was shown that good classification results can be obtained by using phonetic features; more specifically, by using more general features for vowels

The translation of spoken works into respective written scripts is done by speech recognition and language of speech is identified using Automated speech

We presented NERSO, a named entity recognition and disambiguation system using graph-based scoring method for annotating named entities in a given text using

AUTOMATIC TEXT SUMMARIZATION USING SENTENCE SCORING AND RANDOM FOREST ALGORITHM.. Oleh : LA

Shrinivasa Rao explained speech recognition using Hidden Markov Model with MFCC Sub band technique and concluded that with these methods quality metrics of