Effects on transcription errors on supervised learning in speech recognition

Hidden Markov model-based speech recognition systems use supervised learning to train acoustic models. On difficult tasks such as conversational speech, there has been concern over the impact erroneous transcriptions have on the parameter estimation process. This work analyzes the effects of mislabeled data on recognition accuracy. Training is performed using manually corrupted transcriptions, and results are presented on three tasks: TIdigits, alphadigits and switchboard. For alphadigits, with 16% of the training data mislabeled, the performance of the system degrades by 12% relative to the baseline. On switchboard, at 16% mislabeled training data, the performance of the system degrades by 8.5% relative to the baseline. An analysis of these results revealed that the Gaussian mixture model contributes significantly to the robustness of the supervised learning training process.