This paper introduces ASTRA, a novel method for improving Automatic Speech Recognition (ASR) through text injection.Unlike prevailing techniques, ASTRA eliminates the need for sampling to match sequence lengths between speech and text modalities. Instead, it leverages the inherent alignments learned within CTC/RNNT models. This approach offers the following two advantages, namely, avoiding potential misalignment between speech and text features that could arise from upsampling and eliminating the need for models to accurately predict duration of sub-word tokens. This novel formulation of modality (length) matching as a weighted RNNT objective matches the performance of the state-of-the-art duration-based methods on the FLEURS benchmark, while opening up other avenues of research in speech processing.

翻译：本文提出ASTRA，一种通过文本注入改进自动语音识别（ASR）的新方法。与主流技术不同，ASTRA无需通过采样来匹配语音与文本模态的序列长度，而是利用CTC/RNNT模型内部学习到的固有对齐关系。该方法具有以下两个优势：一是避免了上采样可能导致的语音与文本特征错位，二是消除了模型需要精确预测子词单元时长的需求。这种将模态（长度）匹配问题重新定义为加权RNNT目标的新颖方法，在FLEURS基准测试中达到了当前基于时长预测的最优方法性能，同时为语音处理领域开辟了新的研究方向。