Optimisation of multiple feature stream weights for distributed speech processing in mobile environments
Djamel Addou, Selouani Sid‐Ahmed · IET Signal Processing · 2015
Mobile environments are highly influenced by ambient noise that can cause a significant deterioration in speech recognition performance. In this study, a new framework integrating a noise‐robust frontend (FE) in distributed speech recognition (DSR) is presented. Using the Aurora‐2 speech database, the authors evaluate the impact of the proposed multidimensional acoustical analysis on the performance of the Mel‐frequency‐based European Telecommunications Standards Institute‐advanced FE (AFE) combined with the Mel‐line spectral frequencies (MLSFs) robust features for highly noisy speech. The stream weights of the resulting multi‐stream hidden Markov models are optimised automatically by deploying a novel approach based on a discriminative model combination. Finally, these features are effectively transformed and reduced using the Karhunen–Loève transform. The proposed MLSF‐based FE (MLSF‐FE) is shown to exhibit a reduction in the relative error rate. Moreover, the proposed FE provides comparable recognition performance to the current DSR‐AFE available in global system of mobile communications.