Combining Different Recognition Schemes by Analyzing the Noise Condition

The degradation of the human performance is still considerably lower than the corresponding deterioration of automatic recognition systems when comparing the recognition of noisy versus clean speech. It can be observed that the degradation of the recognition rate is dependent on the applied recognition technique and the specific noise condition. We present an approach to select the appropriate recognition scheme by estimating the noise scenario at each speech input. Two different recognition schemes are applied. One is based on the extraction of robust features whereas the other approach contains an adaptation of HMMs (Hidden Markov Models). In case of extracting robust features we investigate the usage of multi-condition HMMs that have been trained on noisy speech signals. We verify that the process of selecting the appropriate scheme and the appropriate set of HMMs can be applied so that the lowest error rate is achieved for each acoustic condition.