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This paper studies how to learn accurate ranking functions from noisy training data for information retrieval.
Therefore, it helps to learn a more accurate model from noisy training data.
Both the DAE and the noisy training approaches corrupt NN inputs by randomly sampled noises.
Figure 4 Performance of noisy training with cafeteria noise injected ( σ =0. 01).
In the second set of experiments, multiple noises are injected when performing noisy training.
Figure 3 Performance of noisy training with car noise injected ( σ =0. 01).
Besides white noise, in general, any noise can be used to conduct the noisy training.
Figure 2 Performance of noisy training with white noise injected ( σ =0. 01).
We proposed a noisy training approach for DNN-based speech recognition.
The 4 × 16 sets of noisy training data were used to construct 64 sets of HMMs.
This paper presents a noisy training approach for DNN-based ASR.
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