基于BiLSTM的低资源老挝语文本正则化任务

A low-resource Lao text regularization task based on BiLSTM

文本正则化TN是语音合成文本前端分析任务中必不可少的工作,老挝语的文本正则化是将老挝语文本中不可读的词NSW转化为可以口头表达的词SFW。目前文本正则化任务尚未在老挝语中开展,主要面临训练数据难获取、部分不可读词存在歧义的问题。针对以上问题,构建了老挝语文本正则化任务的语料,并将老挝语文本正则化任务当作序列标注任务,使用神经网络结合上下文语境预测存在歧义的不可读的老挝语文本,增加自注意力机制加深序列字符间的关系,探究了不同策略引入预训练语言模型的效果,融合各自注意力机制的BiLSTM模型在测试集上达到67.59%的准确率。

Text normalization(TN)is an indispensable work in the front-end analysis task of speech synthesis text.Lao text normalization is to convert non-standard words(NSW)in Lao text into spoken-form words(SFW).At present,the task of text normalization has not yet been carried out in Lao,which mainly faces the problems of difficult acquisition of training data,diversified language expression and text regularization with ambiguity.A text normalization task in Lao is carried out.This task is completed as a sequence tagging task,and neural networks are used to predict NSW with ambiguity in combination with context.The corpus of the Lao text normalization task is constructed,the model results is predicted through the neural network,the self-attention mechanism is increased to deepen the relationship between the sequence characters,and different strategies are explored to introduce the pre-trained language model.An accuracy of 67.59% is achieved on the test set.