Root cepstral analysis: A unified view. Application to speech processing in car noise environments

The performance of speech recognition systems is significantly degraded in the presence of noise. To solve the noise problem, there is a need to reconsider standard approaches by taking into account this new constraint. We first envisage two well-known cepstral representations (parametric and non-parametric) of speech signals and propose a unifying view of both schemes. We introduce a pseudo-autocorrelation domain, which can be interpreted as a “Root-cepstral domain”, and we show how non-parametric cepstral and linear predictive analyses converge to the same optimal solution. Experiments are carried out using an HMM-based isolated word recogniser for speaker-dependent and speaker-independent tasks in car noise environments.