# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 1 Introduction  
Automatic speech recognition is designed to realize the transformation from speech sequences to text sequences. In recent years, compared with the architectures of traditional automatic speech recognition [1], the end-to-end frameworks have shown better recognition effects in the field of speech recognition [2; 3; 4; 5]. Unlike traditional automatic speech recognition models, which consist of acoustics, pronunciation, and language modeling, the end-to-end model has a simpler architecture and a more streamlined training process [6]. However, the current end-to-end model requires a large amount of high-quality labeled data to train the model in order to achieve a good recognition result. This poses a very significant challenge for low-resource languages that lack sufficient labeled data to train end-to-end models [7]. Therefore, it is worthwhile to explore methods for utilizing limited labeled speech data to improve the performance of low-resource automatic speech recognition systems. The current mainstream approach to solve this problem is to use self-supervised learning methods. Self-supervised learning focuses on extracting supervised information from large-scale unlabeled data by using auxiliary tasks, and training the network with this constructed supervised information. This enables the network to learn representations that are useful for downstream tasks [8; 9]. Among them, contrastive learning plays a great role in the field of self-supervised learning, which constructs representations by learning to encode the similarity or dissimilarity of two things [10; 11; 12]. It looks at which pairs of data points are \"similar\" and \"different\" in order to learn higher-level features, andhas a wide range of applications in many fields, such as computer vision [13], emotion recognition [14], natural language processing [15], and domain adaptation [16]. The quality of the representations learned by contrastive learning is largely influenced by the quality of negative samples. In the field of computer vision, two properties of good computer vision representations are alignment and uniform distribution [17], and there are also many ways to select positive samples [18], filter negative samples [19], increase the number of negative samples [20], and generate harder negative samples [21]. A similar approach is used in natural language processing to find high-quality sets of negative samples [21,22]. Many models using contrastive learning methods have also been proposed in the field of automatic speech recognition. Contrastive Predictive Coding (CPC) [23] uses an autoregressive model and noise-contrastive estimation [24] to discard the lower-level information and noise at the lower levels and extract the higher-dimensional speech representations to predict future information. wav2 vec [25] proposes a noise-contrast learning binary classification task using the ideas proposed by CPC, thus allowing wav2 vec to train on a large amount of unlabeled data and achieve better feature extraction results in speech recognition tasks. Based on wav2 vec, vq-wav2 vec [26] realizes the conversion process of feature space from infinite continuous to finite discrete through the proposed quantization module, replaces traditional acoustic features with speech representation generated by combination with BERT, and then trains the speech recognition model in a supervised manner, which improves the latest performance of The Wall Street Journal and TIMIT benchmarks. wav2 vec 2.0 [27] uses comparative learning to propose a new self-supervised architecture, combining the Gumbel softmax quantization module [28] and BERT in vq-wav2 vec into a model, which can be fine-tuned directly in speech downstream tasks with only one step of pre-training, and shows the feasibility of speech recognition with low resources. JUST [29] builds on wav2 vec 2.0 with self-supervised use of contrastive loss and MLM loss and supervised use of RNN-T loss [30] for joint training to achieve higher accuracy in multilingual low-resource situations.