面向对话语音的突显感知自动语音识别
Prominence-aware automatic speech recognition for conversational speech
AI总结：
本文提出一种结合突显检测的突显感知自动语音识别方法，通过微调wav2vec2模型标注语料并训练新型ASR系统，在不影响识别性能的前提下实现了85.53%的突显检测准确率。
摘要：
本文通过结合突显检测与语音识别，研究面向对话式奥地利德语的突显感知自动语音识别（ASR）。首先，通过微调wav2vec2模型来开发突显检测器，以进行词级突显分类。随后，该检测器被用于自动标注大型语料库中的韵律突显。基于这些标注，我们训练了新型突显感知ASR系统，能够同时转录词语及其突显级别。与基线ASR系统相比，整合突显信息并未改变识别性能，同时在识别词序列正确的语句上达到了85.53%的突显检测准确率。本文表明基于transformer的模型能有效编码韵律信息，是对韵律增强ASR的新贡献，具有应用于语言学研究和韵律信息驱动对话系统的潜力。
英文摘要：
This paper investigates prominence-aware automatic speech recognition (ASR) by combining prominence detection and speech recognition for conversational Austrian German. First, prominence detectors were developed by fine-tuning wav2vec2 models to classify word-level prominence. The detector was then used to automatically annotate prosodic prominence in a large corpus. Based on those annotations, we trained novel prominence-aware ASR systems that simultaneously transcribe words and their prominence levels. The integration of prominence information did not change performance compared to our baseline ASR system, while reaching a prominence detection accuracy of 85.53% for utterances where the recognized word sequence was correct. This paper shows that transformer-based models can effectively encode prosodic information and represents a novel contribution to prosody-enhanced ASR, with potential applications for linguistic research and prosody-informed dialogue systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
