More Accurate and Efficient Analysis for Automatic Speech Recognition

更准确、更高效的自动语音识别分析

• 批准号: 914-2013
• 负责人: OShaughnessy, Douglas
• 金额: $ 2.11万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=543917
• 关键词: More; Accurate; Efficient; Analysis; Automatic

Efficient communicating with machines via voice will facilitate interactions that so far have been hindered by awkward interfaces such as keyboards and telephone keypads. People nowadays have increasing need to interact with computers, yet this is still done mostly by typing. Even attempts to seek information by telephone often require many cycles of listening to long messages and then pushing a button, because our capability to do reliable automatic speech recognition (ASR) is quite limited. For some time now, refinements to basic ASR methods established years ago have improved performance, without radical changes to the basic approaches. The recent vast increase in computational power and memory size has led researchers to attack increasingly difficult tasks such as continuously-spoken, very-large-vocabulary, speaker-independent, noisy speech over the telephone. For some limited tasks, e.g., recognizing credit card numbers, or words drawn from medium-sized vocabularies and spoken with frequent pauses, recognition accuracy rises above 99%, and hence practical commercial products are available. However, progress has been slow for the more difficult tasks of recognizing conversational speech or noisy speech. Furthermore, recognition error rates remain high when generalized speaker-independent models are used to decode speakers not used in the training phase.

通过语音与计算机进行有效的通信将有助于迄今为止的互动，键盘和电话键盘等尴尬的接口阻碍了互动。 如今，人们越来越需要与计算机互动，但这仍然主要是通过打字来完成。 即使尝试通过电话寻求信息，通常也需要许多聆听长消息然后按下按钮的周期，因为我们执行可靠的自动语音识别（ASR）的能力非常有限。 一段时间以来，几年前建立的基本ASR方法的改进就提高了性能，而没有对基本方法进行根本性更改。 最近的计算能力和记忆尺寸的大幅增加使研究人员攻击了越来越困难的任务，例如通过电话不断说话，非常大的唱歌，与说话者无关，嘈杂的演讲。 对于某些有限的任务，例如，识别信用卡号或用中型词汇表绘制的单词并经常停顿，识别精度上升到99％以上，因此可以使用实用的商业产品。 但是，对于识别对话言语或嘈杂的言语的更艰巨任务的进步一直很慢。 此外，当使用广义说话者独立模型来解码在训练阶段未使用的说话者时，识别错误率仍然很高。