This paper presents the multi-streams paradigm as a technique for improving speech signal feature set design and as a performance booster for speech recognition systems, based on the continuous-density hidden Markov model (CHMM) framework. In the multi-streams paradigm we are dealing with different feature sets independently to estimate the same task, and then combining their results at a suitable stage. This paradigm combines the strengths of many varied feature vectors to attain better statistical estimation. Under the proposed paradigm the feature vectors are split into three independent streams, and each stream is used to model an independent CHMM. Then the outcomes of these models, when subjected to any speech input, are merged under a certain strategy. This technique alleviates the dominance effect of the features, and reduces the dimensionality of the feature vectors used in each model. The F-ratio technique is used to further reduce the dimensionality of each stream. Experimental results on different datasets show superiority of the developed paradigm over the corresponding single-stream baseline.