通过集成噪声检测架构提升自动语音识别
Enhancing Automatic Speech Recognition Through Integrated Noise Detection Architecture
AI总结：
通过集成噪声检测架构提升语音识别的准确性和噪声辨别能力
摘要：
本研究提出了一种新颖的方法，通过将噪声检测能力直接集成到语音识别架构中，以增强自动语音识别系统。基于wav2vec2框架，所提出的方法引入了一个专门的噪声识别模块，该模块与语音转录同时运行。使用公开可用的语音和环境音频数据集进行实验验证，结果显示转录质量及噪声辨别能力显著提升。改进的系统在词错误率、字符错误率和噪声检测准确性方面均优于传统架构。结果表明，联合优化转录和噪声分类目标能更可靠地在恶劣声学条件下实现语音识别。
英文摘要：
This research presents a novel approach to enhancing automatic speech recognition systems by integrating noise detection capabilities directly into the recognition architecture. Building upon the wav2vec2 framework, the proposed method incorporates a dedicated noise identification module that operates concurrently with speech transcription. Experimental validation using publicly available speech and environmental audio datasets demonstrates substantial improvements in transcription quality and noise discrimination. The enhanced system achieves superior performance in word error rate, character error rate, and noise detection accuracy compared to conventional architectures. Results indicate that joint optimization of transcription and noise classification objectives yields more reliable speech recognition in challenging acoustic conditions.
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