EAGER: Approximate Inference Robust Speech Processing via Sampling

EAGER：通过采样进行近似推理鲁棒语音处理

• 批准号: 1152288
• 负责人: John Walsh
• 金额: $ 15万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1152288&HistoricalAwards=false
• 关键词: EAGER; Approximate; Inference; Robust; Speech

Speech enhancement and speaker recognition are, in principle, related tasks. Knowledge of the speaker can allow for better speech enhancement, while speech with less interference improves the ability to recognize the speaker. Statistical models for these two tasks can be coupled to yield a principled approach to performing both jointly, however the complexity of exact inference in the resulting statistical model, which must straddle a nonlinear feature calculation, is prohibitive. For this reason, approximate inference techniques that sacrifice some performance of exact inference for lower complexity are of interest. This Early Grant for Exploratory Research brings Gibbs sampling approximate inference techniques to bear on joint speech enhancement and speaker recognition for the sake of comparison with several variational approximate inference techniques. The ultimate aim of the research is to partition the attainable complexity and performance space into different regimes dictating which techniques should be used and the performance attainable as a function of complexity. To obtain this partitioning, a thorough empirical evaluation on several large speech corpora will be carried out.Speech enhancement and speaker recognition technology finds multiple uses in defense, commercial, and medical technologies. Multiple technologies would benefit from performance improvement in speech enhancement and speaker recognition brought about by successfully integrating these two interdependent tasks. Potential applications include dialogue systems for controlling e.g. television sets using distant microphones where additive noise is a significant source of distortion. Additionally, speech enhancement is useful for hearing aids. Speaker dependent prior information gains the ability to improve the intelligibility of speech in these devises.

语音增强和说话人识别原则上是相关的任务。 说话者的知识可以允许更好的语音增强，而具有较少干扰的语音提高了识别说话者的能力。 这两个任务的统计模型可以耦合以产生联合执行两者的原则性方法，然而，在所得到的统计模型中精确推断的复杂性（其必须跨越非线性特征计算）是禁止的。 出于这个原因，近似推理技术，牺牲一些性能的精确推理较低的复杂性是感兴趣的。 这个探索性研究的早期补助金带来了吉布斯采样近似推理技术，以承担联合语音增强和说话人识别的比较与几个变分近似推理技术。 研究的最终目的是将可达到的复杂度和性能空间划分为不同的区域，从而决定应该使用哪些技术以及可达到的性能作为复杂度的函数。 为了获得这种划分，将对几个大型语音语料库进行彻底的实证评估。语音增强和说话人识别技术在国防、商业和医疗技术中有着广泛的用途。 通过成功整合语音增强和说话人识别这两项相互依赖的任务，多种技术将受益于这两项任务的性能改善。潜在的应用包括用于控制例如使用远距离麦克风的电视机的对话系统，其中加性噪声是显著的失真源。 此外，语音增强对于助听器是有用的。在这些装置中，说话人相关的先验信息获得了提高语音可懂度的能力。