Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Table 1: Word Error Rate (WER%) on hybrid ASR system consisting of AM and general tri-gram word LM.
['[BOLD] Model/WER(%)', '[BOLD] w trans. errors', '[BOLD] w/o trans. errors']
A triphone HMM-GMM system with speaker-adaptive training was used to generate the alignments and train the neural network acoustic model. The HMM was trained on 122 position dependent phonemes, with 3 silence phonemes and 119 non-silence phonemes. Time delay neural network (TDNN) based AM and general tri-gram word LM had been trained using 40-dimension MFCC & 100-dimension i-vectors and lexicon from training data, respectively. We tested both evaluation sets with and without transcription errors using the well-trained ASR system. The transcription errors lead to a slight performance degradation because they supplied wrong references for ASR.