  This paper presents an end-to-end deep learning model for Automatic Speech
Recognition (ASR) that transcribes Nepali speech to text. The model was trained
and tested on the OpenSLR (audio, text) dataset. The majority of the audio
dataset have silent gaps at both ends which are clipped during dataset
preprocessing for a more uniform mapping of audio frames and their
corresponding texts. Mel Frequency Cepstral Coefficients (MFCCs) are used as
audio features to feed into the model. The model having Bidirectional LSTM
paired with ResNet and one-dimensional CNN produces the best results for this
dataset out of all the models (neural networks with variations of LSTM, GRU,
CNN, and ResNet) that have been trained so far. This novel model uses
Connectionist Temporal Classification (CTC) function for loss calculation
during training and CTC beam search decoding for predicting characters as the
most likely sequence of Nepali text. On the test dataset, the character error
rate (CER) of 17.06 percent has been achieved. The source code is available at:
https://github.com/manishdhakal/ASR-Nepali-using-CNN-BiLSTM-ResNet.
