SBIR Phase I: A Cocktail Party Technology: Real-Time Conversation Separation from Background Voices and Sounds

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是，它将首次使创建语音技术成为可能，这些语音技术在语音和说话人识别方面的性能不会因干扰语音或环境声音的存在而显着降低。这个问题使得许多语音技术无法进入移动的和物联网市场。预计该公司独特的人工智能平台将提供完全可扩展的实时软件解决方案。解决这一挑战将使目前嘈杂的智能手机世界对语音技术（如语音认证）更加现实，迄今为止，语音技术已经避免了这一空间。这个小型企业创新研究第一阶段项目涉及一种新技术，该技术是创新性地将先进的信号处理，广播演播室方法和人工智能技术相结合的结果，以执行语音与背景语音和声音的积极分离。它还根据经验制定的信号相关规则自动修复分离的语音信号的生物特征。该技术已经通过非正式测试证明，在将两人对话与高度重叠的背景声音和一对近距离（几厘米）全向麦克风捕获的声音分离方面，该技术明显优于现有技术。该SBIR第一阶段项目旨在牢固地确立该技术相对于任何其他现有语音分离技术的明显优势。该项目的最终目标是证明，当所提出的技术作为最先进的自动语音识别或最先进的自动说话人识别的前端进行适当优化时，在嘈杂的多语音环境中的识别错误率与在无噪的单语音环境中获得的识别错误率相当。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is that it will for the first time make it possible to create voice technologies whose performance in speech and speaker recognition does not significantly degrade due to the presence of interfering voices or environmental sounds. This issue has kept many voice technologies out of both the mobile and IoT markets. It is expected that the company's unique artificial intelligence platform will deliver a fully scalable, real-time software solution. Solving this challenge will make the currently noisy world of smartphones more realistic for voice technologies (like voice authentication) that to date have avoided the space. This Small Business Innovation Research Phase I project concerns a novel technology that is the result of innovatively combining advanced signal processing, broadcast studio methodologies, and artificial intelligence techniques to perform aggressive separation of voice from background voices and sounds. It also automatically repairs the biometrics of the separated voice signals on the basis of empirically formulated signal-dependent rules. The technology has already been demonstrated through informal tests to be significantly better than existing technologies in separating two-person conversations from highly overlapped background voices and sounds captured on a pair of closely spaced (few centimeters) omnidirectional microphones. This SBIR Phase I project seeks to firmly establish the clear superiority of this technology over any other existing voice separation technology. The ultimate goal of the project is to demonstrate that when the proposed technology is properly optimized as a frontend to state-of-the-art automatic speech recognition or state-of-the-art automatic speaker recognition, the recognition error rates in noisy multi-voice environments are comparable to those obtained in noiseless single-voice environments.