DNN-based performance measures for predicting error rates in automatic speech recognition and optimizing hearing aid parameters

In this study, we look at different performance measures to estimate the word error rates of simulated behind-the-ear hearing aid signals and detect the azimuth angle of the target source in 180-degree spatial scenes. These measures derive from phoneme posterior probabilities produced by a deep neural network acoustic model. However, the more complex the model is, the more computationally expensive it becomes to obtain these measures; therefore, we assess how the model size affects prediction performance. Our findings suggest smaller nets are suitable to predict error rates of more complex models reliably enough to be implemented in hearing aid hardware. (Source: Speech Communication)

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