Transformer based end-to-end modelling approaches with multiple stream inputs have been achieved great success in various automatic speech recognition (ASR) tasks. An important issue associated with such approaches is that the intermediate features derived from each stream might have similar representations and thus it is lacking of feature diversity, such as the descriptions related to speaker characteristics. To address this issue, this paper proposed a novel multi-level acoustic feature extraction framework that can be easily combined with Transformer based ASR models. The framework consists of two input streams: a shallow stream with high-resolution spectrograms and a deep stream with low-resolution spectrograms. The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity. A feature correlation based fusion strategy is used to aggregate both features across the frequency and time domains and then fed into the Transformer encoder-decoder module. By using the proposed multi-level acoustic feature extraction framework, state-of-the-art word error rate of 21.7% and 2.5% were obtained on the HKUST Mandarin telephone and Librispeech speech recognition tasks respectively.