Joint rich and normalized automatic speech recognition (ASR), that produces
transcriptions both with and without punctuation and capitalization, remains a
challenge. End-to-end (E2E) ASR models offer both convenience and the ability
to perform such joint transcription of speech. Training such models requires
paired speech and rich text data, which is not widely available. In this paper,
we compare two different approaches to train a stateless Transducer-based E2E
joint rich and normalized ASR system, ready for streaming applications, with a
limited amount of rich labeled data. The first approach uses a language model
to generate pseudo-rich transcriptions of normalized training data. The second
approach uses a single decoder conditioned on the type of the output. The first
approach leads to E2E rich ASR which perform better on out-of-domain data, with
up to 9% relative reduction in errors. The second approach demonstrates the
feasibility of an E2E joint rich and normalized ASR system using as low as 5%
rich training data with moderate (2.42% absolute) increase in errors.

比较了两种方法来训练具有有限数量的丰富标签数据的无状态变换器的端到端联合丰富和规范自动语音识别系统，其中第一种方法使用语言模型生成规范化训练数据的伪丰富转录，第二种方法使用单个解码器以输出类型为条件。第一种方法提供了更好的外域数据性能，相对误差减少了 9%。第二种方法展示了使用低至 5% 丰富训练数据的端到端联合丰富和规范自动语音识别系统的可行性，误差增加了 2.42% 绝对值。