Tokenization algorithms that merge the units of a base vocabulary into
larger, variable-rate units have become standard in natural language processing
tasks. This idea, however, has been mostly overlooked when the vocabulary
consists of phonemes or Discrete Acoustic Units (DAUs), an audio-based
representation that is playing an increasingly important role due to the
success of discrete language-modeling techniques. In this paper, we showcase
the advantages of tokenization of phonetic units and of DAUs on three
prediction tasks: grapheme-to-phoneme, grapheme-to-DAUs, and unsupervised
speech generation using DAU language modeling. We demonstrate that tokenization
yields significant improvements in terms of performance, as well as training
and inference speed, across all three tasks. We also offer theoretical insights
to provide some explanation for the superior performance observed.

本文展示了在三项预测任务中使用音素单元和 DAU 分词的优势，包括字素到音素、字素到 DAU 和使用 DAU 语言建模的无监督语音生成，并且证明了分词在性能、训练和推理速度上的显著改进，同时提供了理论解释。

探索音频单元标记化的好处

Exploring the Benefits of Tokenization of Discrete Acoustic Units

Unlike hybrid speech recognition systems where the use of tokens was
restricted to phones, biphones or triphones the choice of tokens in the
end-to-end ASR systems is derived from the text corpus of the training data.
The use of tokenization algorithms like Byte Pair Encoding (BPE) and WordPiece
is popular in identifying the tokens that are used in the overall training
process of the speech recognition system. Popular toolkits, like ESPNet use a
pre-defined vocabulary size (number of tokens) for these tokenization
algorithms, but there is no discussion on how vocabulary size was derived. In
this paper, we build a cost function, assuming the tokenization process to be a
black-box to enable choosing the number of tokens which might most benefit
building an end-to-end ASR. We show through experiments on LibriSpeech 100 hour
set that the performance of an end-to-end ASR system improves when the number
of tokens are chosen carefully.

通过在 LibriSpeech 100 小时数据集上的实验证明，当仔细选择标记数量时，端到端 ASR 系统的性能会有所提升。