Effects of discriminative training on the RACAD corpus of the French language spoken in the Canadian province of New-Brunswick

This paper presents a recognition engine especially tailored to the French language spoken in the Canadian pro-vince of New-Brunswick. It studies a global monophone model that handles the linguistic variability found in the province. The study also explores the impact of speaker locality on recognition rate when using the global model. Three models are implemented for each linguistic poles; North-East, North-West, and South-East. The results show respectively 83.58% and 72.66% phone and word recognition rate for Mel frequency Cepstral coefficients, energy, delta and acceleration parameters acoustic models trained discriminatively with maximum mutual information and minimum phone error criterions respectively. Finally, we observe that the general acoustic models are sufficiently generalized to perform uniformly across the three linguistic poles with an average of 82.8% phone recognition rate across the three different acoustic models.