Attention mechanism is one of the most successful techniques in deep learning based Natural Language Processing (NLP). The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers. Grapheme-to-phoneme (G2P) conversion is a task of converting letters (grapheme sequence) to their pronunciations (phoneme sequence). It plays a significant role in text-to-speech (TTS) and automatic speech recognition (ASR) systems. In this paper, we investigate the application of transformer architecture to G2P conversion and compare its performance with recurrent and convolutional neural network based approaches. Phoneme and word error rates are evaluated on the CMUDict dataset for US English and the NetTalk dataset. The results show that transformer based G2P outperforms the convolutional-based approach in terms of word error rate and our results significantly exceeded previous recurrent approaches (without attention) regarding word and phoneme error rates on both datasets. Furthermore, the size of the proposed model is much smaller than the size of the previous approaches.
翻译:变压器网络结构完全以注意机制为基础,它优于神经机器翻译中的序列到序列模型,没有经常性和进化层。 变压器转换是将字母(绘图序列)转换为发音(电话序列)的一项任务。 它在文本到语音(TTS)和自动语音识别(ASR)系统中起着重要作用。 在本文中,我们研究了变压器结构在G2P转换中的应用,并将其性能与经常性和动态神经网络的性能进行比较。在美英CMUDict数据集和NetTalk数据集上评价了电话和字错误率。结果显示,基于G2P的变压器在单词错误率和我们的结果大大超过先前关于两个数据集的单词和电话错误率的经常性方法(没有注意)。此外,拟议模型方法的规模比以往小得多。