A method, comprising: identifying , by an electronic device, one or more segments within a first audio recording that includes one or more non-speech segments and one or more speech segments; generating , by the electronic device, one or more synthetic speech segments that include natural speech audio characteristics and that preserve one or more non-private features of the one or more speech segments; and generating , by the electronic device, an obfuscated audio recording by replacing the one or more speech segments with the one or more synthetic speech segments while maintaining the one or more non-speech segments, wherein the one or more synthetic speech segments prevent recognition of some content of the obfuscated audio recording; and wherein generating the one or more synthetic speech segments comprises: extracting , by the electronic device, the one or more non-private features from the one or more speech segments, wherein the one or more non-private features include at least one of: shimmer or jitter; determining , by the electronic device, generation conditions based on the one or more non-private features by analyzing feature values and evaluating a priority of maintaining each feature based on prior knowledge; and adjusting, by the electronic device, an objective of a learned generator module based on the generation conditions, wherein the learned generator module is trained for generation of the one or more synthetic speech segments and the objective maintains the one or more non-private features. identifying , by an electronic device, one or more segments within a first audio recording that includes one or more non-speech segments and one or more speech segments; generating , by the electronic device, one or more synthetic speech segments that include natural speech audio characteristics and that preserve one or more non-private features of the one or more speech segments; and generating , by the electronic device, an obfuscated audio recording by replacing the one or more speech segments with the one or more synthetic speech segments while maintaining the one or more non-speech segments, wherein the one or more synthetic speech segments prevent recognition of some content of the obfuscated audio recording; and wherein generating the one or more synthetic speech segments comprises: extracting , by the electronic device, the one or more non-private features from the one or more speech segments, wherein the one or more non-private features include at least one of: shimmer or jitter; determining , by the electronic device, generation conditions based on the one or more non-private features by analyzing feature values and evaluating a priority of maintaining each feature based on prior knowledge; and adjusting, by the electronic device, an objective of a learned generator module based on the generation conditions, wherein the learned generator module is trained for generation of the one or more synthetic speech segments and the objective maintains the one or more non-private features. extracting , by the electronic device, the one or more non-private features from the one or more speech segments, wherein the one or more non-private features include at least one of: shimmer or jitter; determining , by the electronic device, generation conditions based on the one or more non-private features by analyzing feature values and evaluating a priority of maintaining each feature based on prior knowledge; and adjusting, by the electronic device, an objective of a learned generator module based on the generation conditions, wherein the learned generator module is trained for generation of the one or more synthetic speech segments and the objective maintains the one or more non-private features. <EOS>