For distinctively transporting voice data with silence suppression over Asynchronous Transfer Mode (ATM) networks via the Variable Bit Rate (VBR) service, the problem of jitter introduced from the network often renders the speech unintelligible. It is thus indispensable to offer intramedia synchronization to remove jitter while retaining minimal playout delay. In this paper, we propose a neural-network-based intra-voice synchronization mechanism, called the Intelligent Voice Smoother (IVoS). IVoS is composed of three components: Smoother Buffer, Neural Network (NN) Traffic Predictor, and Constant Bit Rate (CBR) Enforcer. Newly arriving frames, being assumed to follow a generic Markov-Modulated Bernoulli Process (MMBP), are queued in the Smoother Buffer. The NN Traffic Predictor employs an on-line-trained Back Propagation Neural Network (BPNN) to predict three traffic characteristics of every newly encountered talkspurt period. Based on the predicted characteristics, the CBR Enforcer derives an adaptive buffering delay by means of a near-optimal, simple, closed-form formula. It then imposes such delay on the playout of the first frame in the talkspurt period. The CBR Enforcer in turn regulates CBR-based departures for the remaining frames of the talkspurt, aimed at assuring minimal mean and variance of Distortion of Talkspurts (DOT) and mean playout Delay (PD). Simulation results reveal that, compared to three other playout approaches, IVoS achieves superior playout yielding negligible DOT and PD irrespective of traffic variation.