The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are represented jointly. In ASR, this idea has found application as joint speech-text encoders that can scale to the capacities of very large parameter models by being trained on both unpaired speech and text. While these methods show promise, they have required special treatment of the sequence-length mismatch inherent in speech and text, either by up-sampling heuristics or an explicit alignment model. In this work, we offer evidence that joint speech-text encoders naturally achieve consistent representations across modalities by disregarding sequence length, and argue that consistency losses could forgive length differences and simply assume the best alignment. We show that such a loss improves downstream WER in both a large-parameter monolingual and multilingual system.

翻译：过去一年见证了文本提示图像生成的显著进展，其前提是基于跨模态表示空间的概念，在该空间中文本与图像领域被联合表示。在自动语音识别中，这一思想已作为联合语音-文本编码器得到应用，通过训练无配对语音和文本，此类编码器可扩展至超大规模参数模型的容量。尽管这些方法展现出潜力，但它们需要对语音与文本固有的序列长度不匹配问题施加特殊处理，要么采用上采样启发式方法，要么使用显式对齐模型。本研究提出证据表明，联合语音-文本编码器通过忽略序列长度自然实现了跨模态的一致性表示，并论证一致性损失能够容忍长度差异，仅需假设最优对齐即可。我们证明，此类损失能够在大参数单语言和多语言系统中改善下游词错误率性能。