Automatic Speech Recognition (ASR) is crucial for preserving minority languages, promoting inclusivity, and supporting education. Wav2vec2.0 Model, pre-trained through self-supervised learning, is effective for low-resource language speech recognition. Thus, this study investigates different learning strategies, recognizers, and frameworks to improve ASR performance for low-resource languages. First, we compared five learning strategies for low-resource language speech recognition using the wav2vec2.0 model. The Freeze-Transformer strategy, which fixes the CNN and low-layer Transformer blocks, achieved the lowest Character Error Rate (CER). Next, we evaluated five types of recognizers, including fully connected layers, MLP, RNN, LSTM, and GRU. The bi-GRU recognizer performed the best, achieving the lowest CER. Finally, we tested an Encoder-Decoder model with wav2vec2.0 as the encoder and a Transformer-decoder as the decoder. The results showed that the recognition performance did not improve with this model, even with a large amount of training data.