Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly. Existing audio adversarial examples are typically constructed by adding constrained perturbations on benign audio inputs. Such attacks are therefore generated with an audio dependent assumption. For the first time, we propose the speech synthesising based attack (SSA), a novel threat model that constructs audio adversarial examples entirely from scratch (i.e., without depending on any existing audio) to fool cutting edge ASR models. To this end, we introduce a conditional variational auto-encoder (CVAE) as the speech synthesiser. Thereby, we formulate the adversarial audio synthesising task as an optimisation problem via searching in the hidden space of CVAE. Experiments on three dataset (i.e., audio mnist, common voice, and librispeech) show that our method could synthesise audios that are naturally sounded but misleading to the start-of-the-art ASR models. Source code will be available upon acceptance.