Automatic word recognition in cars

The paper compares, on a database recorded in a car, a number of signal analysis and speech enhancement techniques as well as some approaches to adapt speech recognition systems. It is shown that a new nonlinear spectral subtraction associated with Mel frequency cepstral coefficients (MFCC) is an adequate compromise for low-cost integration. The Lombard effect is analyzed and simulated. Such a simulation is used to derive realistic training utterances from noise-free utterances. Adapting a continuous-density hidden Markov model (CDHMM) to these artificially generated training samples yields a very high performance with respect to that achieved within the ESPRIT adverse environment recognition of speech (ARS) project, i.e., an average of 1% error for all driving conditions. Finally, the paper shows, both theoretically and experimentally, that whatever the noise estimation technique is, it is better to add this noise estimate to the reference clean models than to subtract it from the noisy data