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cs.CL2023
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer
Jin Qiu, Lu Huang, Boyu Li +3
Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming…
cs.CL2023
Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring
Kaiqi Fu, Shaojun Gao, Shuju Shi +3
Speech fluency/disfluency can be evaluated by analyzing a range of phonetic and prosodic features. Deep neural networks are commonly trained to map fluency-related features into th…
cs.CL2023
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion
Chunfeng Wang, Peisong Huang, Yuxiang Zou +4
As a key component of automated speech recognition (ASR) and the front-end in text-to-speech (TTS), grapheme-to-phoneme (G2P) plays the role of converting letters to their correspo…