Quantile based histogram equalization for noise robust speech recognition

This work describes an algorithm to increase the noise robustness of automatic speech recognition systems. In many practical applications recognition systems have to work in adverse acoustic environment conditions. Distortions and noises caused by the transmission are typical for telephone applications. Considerable amounts of varying background noise are a problem for all mobile applications such as cellular phones or speech controlled systems in cars. Automatic systems are much more sensitive to the variabilities of the acoustic signal than humans. Whenever there is a mismatch between the distribution of the training data and the data that is to be recognized, the recognition word error rates will increase. There are three possible ways of dealing with such a mismatch during the recognition process: adapting the recognizer’s model parameters to the current noise condition, using a modified likelihood calculation that is invariant to the distortions caused by the noise, and reducing the influence of the noise during feature extraction. Within this work a feature extraction method is investigated. The goal was to develop a computationally inexpensive method that can be applied in real time online systems. It should not require any prior assumptions about the noise conditions that are to be expected or the kind of training data available. And it should be independent from the actual recognition system that will use the features. Quantile based histogram equalization improves the recognition performance in noisy conditions by applying a non–linear parametric transformation function during feature extraction. It reduces an eventual mismatch between the current recognition data and the distribution of the data the system was trained on. Based on the quantiles of the cumulative distributions, the parameters of the transformation function can be reliably estimated from small amounts of data. By the way they are defined quantiles are independent from the scaling, range, and amount of data. Thus making the method independent from prior assumptions about the training and recognition data. The approach is integrated into a modified Mel cepstrum feature extraction, in which the logarithm is replaced by a root function to increase the noise robustness. The actual transformation that is proposed in this work consists of two steps. First, a power function transformation is applied to each output of the Mel–scaled filter–bank, then neighboring filter are channels combined linearly. These transformation steps can be added to the feature extraction using a moving window implementation that does not require more delay than a conventional mean and variance normalization. To investigate the genericity of the approach and the proposed setup, experimental evaluations have been carried out with different speech recognition systems, on several databases with different levels of complexity, ranging from digit strings (SpeechDat Car) to larger vocabulary isolated word (Car Navigation) and continuous speech recognition tasks (Wall Street Journal with added noise). Consistent recognition results were observed on all databases. The modified feature extraction, with the root instead of the logarithm, already outperformed the original baseline on noisy data. Filter channel specific quantile equalization always improved these results, yielding relative improvements between of 5% and 50%, depending on the recognition task and the mismatch of the data. Finally, the combination of neighboring filter channels was able to reduce the error rates somewhat further, especially if the noise, like car noise, was band limited.