Direct acoustics-to-word (A2W) systems for end-to-end automatic speech
recognition are simpler to train, and more efficient to decode with, than
sub-word systems. However, A2W systems can have difficulties at training time
when data is limited, and at decoding time when recognizing words outside the
training vocabulary. To address these shortcomings, we investigate the use of
recently proposed acoustic and acoustically grounded word embedding techniques
in A2W systems. The idea is based on treating the final pre-softmax weight
matrix of an AWE recognizer as a matrix of word embedding vectors, and using an
externally trained set of word embeddings to improve the quality of this
matrix. In particular we introduce two ideas: (1) Enforcing similarity at
training time between the external embeddings and the recognizer weights, and
(2) using the word embeddings at test time for predicting out-of-vocabulary
words. Our word embedding model is acoustically grounded, that is it is learned
jointly with acoustic embeddings so as to encode the words' acoustic-phonetic
content; and it is parametric, so that it can embed any arbitrary (potentially
out-of-vocabulary) sequence of characters. We find that both techniques improve
the performance of an A2W recognizer on conversational telephone speech.

通过将最终的 AWE 识别器的 pre-softmax 权重矩阵视为词嵌入向量矩阵，并利用外部训练的词嵌入向量来改善其质量，我们提出了两种思路：（1）在训练中在外部嵌入向量与识别器权重之间强制相似性，（2）在测试时使用词嵌入向量预测生僻词以提高直接声学到单词的性能。