CRDNN-BiLSTM Knowledge Distillation Model Towards Enhancing the Automatic Speech Recognition

Numerous automatic speech recognition (ASR) models have been developed in recent years, but they suffer from the drawback of being large models that take more time to train and are difficult to deploy on devices. Knowledge distillation has been used to reduce the size of current learning models while keeping up the efficiency across a range of applications. As a result, the knowledge distillation for the ASR model has been suggested in this paper to make the training process simpler and faster than the existing model. The knowledge gained from training a teacher acoustic model is transferred to the student acoustic model to improve its performance. With the help of this work, the ASR models can be trained effectively with fewer tiresome tasks. Graphical results show that this framework efficiently trains the audio input. The experimental results inferred that the proposed model employing knowledge distillation is efficient in speech recognition by achieving a Word Error Rate of 1.21% on LibriSpeech Corpus dev-clean and 2.23% on LibriSpeech Corpus test-clean.