Improving basic methods of automatic speech recognition

改进自动语音识别的基本方法

• 批准号: 914-2008
• 负责人: OShaughnessy, Douglas
• 金额: $ 3.83万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=513008
• 关键词: Improving; basic; methods; automatic; speech

The biggest problem in automatic speech recognition (ASR) is robustness - making sure that ASR models are general enough. A robust ASR system should be able to decode speech from any speaker, in any reasonable environment. In practice, speakers other than those that trained the ASR models lead to a significant decrease in ASR accuracy. As well, environmental noise (e.g., weather, other talkers, cars) and communication distortions (e.g., with mobile phones) degrade ASR performance, often severely. Even simple microphone substitution reduces accuracy. Human listeners, in contrast, often can adapt rapidly to all these difficulties, which strongly suggests that major flaws exist in current ASR schemes. While it is not necessary to directly mimic human perception when designing ASR systems, much of what we know about human speech production and perception has yet to be integrated into practical ASR. ASR can be greatly improved by better integration of structural information with the current stochastic methodology.

自动语音识别（ASR）中最大的问题是鲁棒性-确保ASR模型足够通用。 一个健壮的ASR系统应该能够在任何合理的环境中解码来自任何说话者的语音。 在实践中，除了训练ASR模型的那些人之外的说话者会导致ASR准确性的显着下降。 此外，环境噪声（例如，天气、其他谈话者、汽车）和通信失真（例如，使用移动的电话）通常严重地降低ASR性能。 即使是简单的麦克风替换也会降低准确性。 相比之下，人类听众通常可以迅速适应所有这些困难，这强烈表明当前ASR方案存在重大缺陷。 虽然在设计ASR系统时没有必要直接模仿人类的感知，但我们对人类语音产生和感知的大部分了解尚未整合到实际的ASR中。 通过更好地整合结构信息和当前的随机方法，可以大大提高ASR。