Following the work of Cairns et al. (2016), we aim at correcting mortality estimates based on fertility data. As already conjectured by Richards (2008), the computation of exposure to risk can suffer from errors for cohorts born in years in which births are fluctuating. In this context, we first point our attention to the Human Mortality Database (HMD), the reference mortality data provider. While comparing period and cohort mortality tables, we highlight the presence of anomalies in period ones in the form of isolated cohort effects. Our investigation of the HMD methodology exhibits a strong assumption of uniform distribution of births that is specific to period tables, therefore likely to be at the core of the asymmetry between both. Based on the idea of Cairns et al. (2016) regarding the construction of kind of "data quality indicator", we make a new and intensive exploitation of the Human Fertility Database (HFD), which is from our point of view a crucial source as it represents the perfect counterpart of the HMD in terms of fertility. This indicator is then used to construct corrected period mortality tables for several countries, which we analyze on both an historical and prospective point of view. Our main conclusions relate to the reduction of volatility of mortality improvement rates, the impact in the use of cohort parameters in stochastic mortality models, as well as a better fit of corrected tables by classical mortality models.