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Automatic detection of syllable stress using sonority based prominence features for pronunciation evaluation.

, , and . ICASSP, page 5845-5849. IEEE, (2017)

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Low Resource Automatic Intonation Classification Using Gated Recurrent Unit (GRU) Networks Pre-Trained with Synthesized Pitch Patterns., , and . INTERSPEECH, page 959-963. ISCA, (2019)ASR Inspired Syllable Stress Detection for Pronunciation Evaluation Without Using a Supervised Classifier and Syllable Level Features., , and . INTERSPEECH, page 924-928. ISCA, (2019)Multilingual and code-switching ASR challenges for low resource Indian languages., , , , , , , , , and 12 other author(s). CoRR, (2021)Concatenative Articulatory Video Synthesis Using Real-Time MRI Data for Spoken Language Training., , and . ICASSP, page 4999-5003. IEEE, (2018)A comparative study on the effect of different codecs on speech recognition accuracy using various acoustic modeling techniques., , , , , , and . NCC, page 1-6. IEEE, (2017)A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection., , and . Speech Commun., (2016)An Improved Goodness of Pronunciation (GoP) Measure for Pronunciation Evaluation with DNN-HMM System Considering HMM Transition Probabilities., , , and . INTERSPEECH, page 954-958. ISCA, (2019)Comparison of automatic syllable stress detection quality with time-aligned boundaries and context dependencies., , and . SLaTE, page 79-83. ISCA, (2019)A Study on Native American English Speech Recognition by Indian Listeners with Varying Word Familiarity Level., , , , and . O-COCOSDA, page 13-18. IEEE, (2021)Analysis of Natural Language Understanding Systems with L2 Learner Specific Synthetic Grammatical Errors Based on Parts-of-Speech., , , and . SPECOM (1), volume 14338 of Lecture Notes in Computer Science, page 442-454. Springer, (2023)