This paper presents an end-to-end model designed to improve automatic speech
recognition (ASR) for a particular speaker in a crowded, noisy environment. The
model utilizes a single-channel speech enhancement module that isolates the
speaker's voice from background noise, along with an ASR module. Through this
approach, the model is able to decrease the word error rate (WER) of ASR from
80% to 26.4%. Typically, these two components are adjusted independently due to
variations in data requirements. However, speech enhancement can create
anomalies that decrease ASR efficiency. By implementing a joint fine-tuning
strategy, the model can reduce the WER from 26.4% in separate tuning to 14.5%
in joint tuning.

该论文提出了一种端到端模型，旨在改进在拥挤嘈杂环境中特定讲话者的自动语音识别（ASR）。该模型利用单通道语音增强模块将讲话者的声音与背景噪音隔离，并结合了 ASR 模块。通过这种方法，该模型能够将 ASR 的识别错误率（WER）从 80％降低到 26.4％。通常，由于数据要求的差异，这两个组件是独立调整的。然而，语音增强可能会引起降低 ASR 效率的异常情况。通过实施联合精调策略，该模型可以将单独调整中的 WER 从 26.4％降低到 14.5％。