Sentence examples for multiple noise injection from inspiring English sources

Figure 5 Performance of multiple noise injection.

Figure 6 Performance of multiple noise injection with clean speech involved in training.

We note that the noisy training with multiple noise injection resembles the multi-condition training: Both involve training speech data under multiple noise conditions.

It can be seen that with the noisy training, almost all the WER reductions (except in the clean speech case) are positive, which means that the multiple noise injection improves the system performance in almost all the noise conditions.

As a summary, the noisy training based on multiple noise injection is effective in learning patterns of multiple noise types, and it usually leads to significant improvement of ASR performance on speech data corrupted by the noises that have been learned.

This paper employs this theory and contributes in two aspects: first, we examine the behavior of noise injection in DNN training, a more challenging task involving a huge amount of parameters; second, we study mixture of multiple noises at various levels of signal-to-noise ratios (SNR), which is beyond the conventional noise injection theory that assumes small and Gaussian-like injected noises.

The future work involves investigating various noise injection approaches (e.g., weighted noise injection) and evaluating more noise types.

We first investigate the effect of white noise injection.

For the car noise corruption, however, the white noise injection does not show any benefit.

We choose the car noise and the cafeteria noise in this experiment to investigate the color noise injection.

In addition, new noise injection circuits for ternary logic are also presented to perform noise immunity analysis.