# The XMUSPEECH System for Accented English Automatic Speech Recognition
###### Abstract  
In this paper, we present the XMUSPEECH systems for Track 2 of the Interspeech 2020 Accented English Speech Recognition Challenge (AESRC2020). Track 2 is an Automatic Speech Recognition (ASR) task where the non-native English speakers have various accents, which reduces the accuracy of the ASR system. To solve this problem, we experimented with acoustic models and input features. Furthermore, we trained a TDNN-LSTM language model for lattice rescoring to obtain better results. Compared with our baseline system, we achieved relative word error rate (WER) improvements of 40.7% and 35.7% on the development set and evaluation set, respectively. AESRC2020; i-vector; x-vector; multistream CNN +
Footnote †: journal: Applied Sciences  
Article  
The standard English ASR system has been able to obtain a high recognition accuracy and meet the commercial requirements of certain scenarios. However, numerous studies have shown that accent affects the accuracy of ASR to a large extent, [PERSON] et al. [1] quantified the bias of a SOTA ASR system, and the result showed the accuracy of the system for native speakers in different regions varies greatly owing to accent. [PERSON] et al. [2] showed a well-trained ASR model performed badly on other accents corpus. Due to the inconsistency of the accent itself, the variability of speech speed and phoneme pronunciation, as well as the scarcity of accented speech data, accented English recognition is still a challenging subject. The Interspeech 2020 Accented English Speech Recognition Challenge (AESRC2020) ([[https://www.datatang.com/INTERSPEECH2020](https://www.datatang.com/INTERSPEECH2020)]([https://www.datatang.com/INTERSPEECH2020](https://www.datatang.com/INTERSPEECH2020)) (accessed on 20 August 2021)) includes two tracks, Accent Recognition (AR) Track and Accented English ASR Track. This paper describes the XMUSPEECH systems for the Accented English ASR Track, which is a task to train an ASR model while the data is composed of diverse English accents. In the field of multi-accent ASR, the mainstream approaches are the DNN-HMM model and the end-to-end model. The end-to-end model is a hot topic of current research [3; 4; 5; 6; 7; 8]; these studies have made significant contributions to end-to-end pretraining, data augmentation, adversarial learning, unsupervised training, etc. The end-to-end architecture adopts a single network structure, which is simple compared with the traditional hybrid model and can avoid the bias caused by multi-module training, thus ensuring the global optimum to the greatest extent. However, end-to-end training requires a large amount of data, and this single network structure also leads to relatively low interpretability of the model. The DNN-HMM model follows the traditional speech recognition framework, including an acoustic model, language model, and lexicon, which constitute the decoder, and the input acoustic features are converted into text by the decoder. The acoustic model acts to align phonemes and acoustic features, it accounts for most of the computational overhead of the whole system and determines the performance of speech recognition systems. Thereis much interest in developing the acoustic model [9; 10; 11], specifically, [PERSON] et al. [11] proposed a convolutional neural network (CNN)-based architecture, which had variable filter sizes along the frequency band of the audio utterances, and the overall accuracy for accent speech recognition surpassed all of the prior work. [PERSON] et al. [9] used TDNN [12] as an acoustic model for accented English speech recognition and achieved a relatively low average WER. The TDNN [12] is a 1-D convolution neural network, it performs well on temporal tasks such as ASR. On the basis of TDNN [12], [PERSON] et al. proposed [13] TDNN-F, which has fewer parameters and can be trained with a deeper level. Our system adopts TDNN-F [13] as an acoustic model. We have two considerations for adopting this structure.