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Automatic Phoneme Recognition

Automatic Phoneme Recognition (APR) involves converting spoken language into a sequence of phonemes, which are the distinct units of sound that distinguish one word from another in a given language. It is designed to transcribe spoken words into their textual phonetic representations in real-time, enabling detailed analysis of speech patterns, pronunciation, and linguistic nuances. The goal of Automatic Phoneme Recognition is to accurately identify and transcribe phonemes, considering variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality. This technology is crucial for linguistic research, speech therapy, language learning applications, and enhancing the performance of speech recognition systems.

Papers

Showing 15 of 5 papers

TitleStatusHype
Vibravox: A Dataset of French Speech Captured with Body-conduction Audio SensorsCode1
A Comprehensive Survey on Bengali Phoneme Recognition0
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling0
Speech Data Augmentation for Improving Phoneme Transcriptions of Aphasic Speech Using Wav2Vec 2.0 for the PSST Challenge0
Study of Phonemes Confusions in Hierarchical Automatic Phoneme Recognition System0
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