Reconhecimento de Voz Contínua com Atributos PNCC e Métodos de Robustez WD e MAP

The degradation of the speech signal due to adverse conditions generates low accuracy rates in speech recognition systems. The authors propose mixing two methods: pre-extraction of attributes for speech enhancement and post-extraction of attributes for features compensation. According to their main focus, they are fundamentally oriented to minimize the misfit caused by noise insertion in the speech signal. These methods will be applied before and after the extraction of attributes, respectively, therefore allowing the best possible estimation of the clear signal from its degraded version.