One of the challenges in using Kaldi is its limited flexibility in implementing new DNN models. To address this issue, researchers have developed various extensions and integrations with other deep learning frameworks, such as PyTorch and TensorFlow. These integrations allow developers to take advantage of the flexibility and ease of use provided by these frameworks while still benefiting from Kaldi's efficient decoding capabilities. Projects like PyTorch-Kaldi and Pkwrap have been developed to bridge the gap between Kaldi and popular deep learning frameworks, enabling users to design custom model architectures with ease.