Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
Table 2: ATIS test set results on ASR word error rate, intent detection error, and slot filling F1 score with noisy speech input.
['[BOLD] ASR Model (with LibriSpeech AM)', '[BOLD] WER', '[BOLD] Intent Error', '[BOLD] F1 Score']
Model performance is evaluated in terms of ASR word error rate (WER), intent classification error, and slot filling F1 score. Using the rescored ASR outputs (12.59% WER) as input to the joint training SLU model, the intent classification error increased by 2.87%, and slot filling F1 score dropped by 7.77% comparing to the setup using true text input. The performance degradation is expected as we used a more challenging and realistic setup with noisy speech input.