# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 2 Related Work  
As a front-end technology for speech signal processing, language identification plays a vital role in speech recognition and other related fields, mainly speech translation, public safety, and multilingual dialogue systems [9]. Language identification is a typical classification problem, and different features can have different influences. As a result, the corresponding model for each language is trained and saved based on the appropriate algorithm [10]. In the recognition process, the features are first extracted and fed into the classification model, and the language type of the speech signal is determined based on the similarities [11]. Traditional acoustic models, such as the Gaussian mixture model-universal background model (GMM-UBM) [12], the hidden Markov model (HMM), etc. [13], were used for language identification, but these often require an extensive number of training parameters to capture the feature space's complexity. To address this issue, [PERSON] et al. [14] employed the SVM algorithm to classify the GMM mean supervector of speech (GMM-SVM). Language scholars also developed a language identification method that utilizes i-vectors [15]. This approach involves obtaining i-vectors from speech and employing a back-end discrimination algorithm for language identification. By utilizing this method, the complexity of multilingual modeling can be reduced while still achieving exceptional performance. In recent years, researchers used deep learning to extract the deep bottleneck features (DBF) [16] of speech signals. This method used i-vectors instead of acoustic features and GMM-UBM to capture language information more effectively. However, it also increases the complexity of the model. Researchers have subsequently proposed end-to-end language identification systems based on different neural network architectures. Firstly, [PERSON] et al. [5] applied deep neural networks (DNNs) to short-time language identification. [PERSON] et al. [17] proposed a long and short-term memory recurrent neural network (LSTM-RNN) for automatic language identification, which effectively solved the problem of gradient disappearance in RNNs. Still, the model was complex and time-consuming. [PERSON] et al. [18] built an end-to-end language identification system based on bidirectional LSTM (BiLSTM), which effectively takes into account the past and future information of speech. [PERSON] et al. [19] used a bidirectional gated recurrent unit (GRU) network for multi-categorical language identification, which has a more straightforward structure and improved recognition rate compared to the Bi-LSTM network. Another popular approach for language identification is a convolutional neural network (CNN) [20], which extracts local features from speech signals and enhances language identification. CNNs are trained on the spectral map of the raw audio signal. This method involves end-to-end learning with minimal pre-processing, as the neural network can directly map the original data to the final output without relying on the traditional machine learning pipeline. In 2016, [PERSON] et al. [21] applied an attention mechanism model to language identification systems. This mechanism selects the most relevant speech features for language identification and improves the recognition performance of the network. In 2017, [PERSON] et al. [22] combined a convolutional network with a recurrent neural network (CRNN) for language identification and proposed a CNN-BiLSTM network [23], which achieved higher accuracy. Although BiLSTM does improve the recognition accuracy, it has some problems, such as an inability to parallelize operations and poor modeling of the effects of hierarchical information. To address these issues, [PERSON] et al. in 2021 [24] proposed an encoder approach based on the \"transformer architecture\" applied to the language identification task using speech-directed information. In the same year, [PERSON] et al. applied the unsupervised learning speech pre-training method [25] to the language identification system.