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We present a modified soft-output Viterbi algorithm (MSOVA) that performs as good as the a posteriori probability (APP) algorithm with a complexity similar to that of the conventional SOVA algorithm. The idea behind the MSOVA centers around reducing the inherent correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA), where the latter is typically much higher than its APP counterpart. The proposed algorithm employs two attenuators, one applied directly to the output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). We examine the MSOVA on additive white Gaussian noise (AWGN) and fading channels. We show that the MSOVA provides improvements of about 0.8 to 1.0 dB at Pb = 10-5 in AWGN over the conventional SOVA, and is only about 0.1 dB away from the APP. It also provides improvements of 1.4 to 2.0 dB at Pb = -5 on fading channels.