论文标题
多种语言词嵌入模型用于处理零资源语言
Multilingual acoustic word embedding models for processing zero-resource languages
论文作者
论文摘要
声词嵌入是可变长度语音段的固定维表示。在未标记的语音是唯一可用资源的设置中，此类嵌入可以在“零资源”语音搜索，索引和发现系统中使用。在这里，我们建议在来自多种资源良好的语言的标记数据上训练单个监督的嵌入模型，然后将其应用于看不见的零资源语言。对于这种转移学习方法，我们考虑了两种多语言的复发性神经网络模型：一种对所有培训语言的联合词汇训练的歧视性分类器，以及一个对通信自动编码器进行了训练，可以重建单词对。我们使用单词歧视任务在六个目标零资源语言上测试这些任务。当对七种资源良好的语言进行培训时，这两种模型的性能类似和跑赢大盘的无监督模型接受了零资源语言的训练。仅使用单个培训语言，第二个模型的工作方式更好，但是性能更多地取决于特定的培训 - 测试语言对。
Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training--testing language pair.
