Recent studies have been revisiting whole words as the basic modelling unit
in speech recognition and query applications, instead of phonetic units. Such
whole-word segmental systems rely on a function that maps a variable-length
speech segment to a vector in a fixed-dimensional space; the resulting acoustic
word embeddings need to allow for accurate discrimination between different
word types, directly in the embedding space. We compare several old and new
approaches in a word discrimination task. Our best approach uses side
information in the form of known word pairs to train a Siamese convolutional
neural network (CNN): a pair of tied networks that take two speech segments as
input and produce their embeddings, trained with a hinge loss that separates
same-word pairs and different-word pairs by some margin. A word classifier CNN
performs similarly, but requires much stronger supervision. Both types of CNNs
yield large improvements over the best previously published results on the word
discrimination task.

研究探讨了在语音识别和查询应用中将整个单词作为基本建模单位而不是音素单位，构建了可变长度语音片段到固定维度空间向量的函数以及比较了几种旧的和新的方法来解决单词辨识问题，最后制定了一种监督学习算法利用了成对单词信息训练 Siamese 卷积神经网络以得出单词的嵌入向量。研究发现该算法在单词辨识任务上取得了比以往出版物更好的结果。