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.

该论文介绍了 ASTRA，一种通过文本注入来改进自动语音识别（ASR）的新方法，它消除了在语音和文本模态之间匹配序列长度时采样的需要，并利用 CTC/RNNT 模型内部学习到的固有对齐性。这种方法有两个优点，即避免由上采样导致的语音和文本特征之间潜在的对齐问题，以及消除模型准确预测子词标记持续时间的需求。该新颖的模态（长度）匹配公式将加权的 RNNT 目标与基于持续时间方法在 FLEURS 基准测试上的最新技术性能相匹配，同时开辟了语音处理领域的其他研究方向。

ASTRA：无需采样的 ASR 语音和文本表示对齐

ASTRA: Aligning Speech and Text Representations for Asr without Sampling

Text injection for automatic speech recognition (ASR), wherein unpaired
text-only data is used to supplement paired audio-text data, has shown
promising improvements for word error rate. This study examines the use of text
injection for auxiliary tasks, which are the non-ASR tasks often performed by
an E2E model. In this work, we use joint end-to-end and internal language model
training (JEIT) as our text injection algorithm to train an ASR model which
performs two auxiliary tasks. The first is capitalization, which is a
de-normalization task. The second is turn-taking prediction, which attempts to
identify whether a user has completed their conversation turn in a digital
assistant interaction. We show results demonstrating that our text injection
method boosts capitalization performance for long-tail data, and improves
turn-taking detection recall.

本研究通过使用文本注入算法来训练自动语音识别模型，以解决长尾数据的大写问题，并改善对话机器人中的交互转移预测。