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End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition
2019
Speech Communication
In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a crucial step. This is typically achieved by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then training a classifier such as artificial neural
doi:10.1016/j.specom.2019.01.004
fatcat:ch64ijeyzbcrvhje2d4glbwaxe