This paper presents a method for selecting appropriate synthetic speech
samples from a given large text-to-speech (TTS) dataset as supplementary
training data for an automatic speech recognition (ASR) model. We trained a
neural network, which can be optimised using cross-entropy loss or Arcface
loss, to measure the similarity of a synthetic data to real speech. We found
that incorporating synthetic samples with considerable dissimilarity to real
speech, owing in part to lexical differences, into ASR training is crucial for
boosting recognition performance. Experimental results on Librispeech test sets
indicate that, in order to maintain the same speech recognition accuracy as
when using all TTS data, our proposed solution can reduce the size of the TTS
data down below its $30\,\%$, which is superior to several baseline methods.

本文提出了一种方法，通过训练神经网络来选择合适的合成语音样本，使其作为自动语音识别模型的辅助训练数据，这在训练中包括有实际的标注数据和合成的数据。在实验中，我们发现将与真实语音具有较大差异性的合成样本（由于词汇差异等原因）纳入到训练中对于提高语音识别性能至关重要，同时，我们的方法可以显著减小文本 — 语音转换（TTS）数据的大小。