ITR: Dynamics-based Speech Segregation

ITR：基于动力学的语音分离

• 批准号: 0081058
• 负责人: DeLiang Wang
• 金额: $ 45万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0081058&HistoricalAwards=false
• 关键词: ITR; Dynamics; based; Speech; Segregation

A typical auditory scene contains multiple simultaneous events, and a remarkable feat of the auditory nervous system is its ability to disentangle the acoustic mixture and group the acoustic energy from the same event. This fundamental process of auditory perception is called auditory scene analysis. Of particular importance in auditory scene analysis is the separation of speech from interfering sounds, or speech segregation. Speech segregation remains a largely unsolved problem in auditory engineering and speech technology. In this project, the P1 seeks to develop a dynamics-based system for speech segregation using perceptual and neural principles. Auditory grouping will be based on oscillatory correlation, whereby phases of neural oscillators encode the binding of auditory features. The investigation will consist of subsequent stages of computation, starting from simulated auditory periphery composed of cochlear filtering and hair cell transduction. A mid-level representation will be formed by computing auto- and cross-correlation of filter channels. A stage of segment formation then creates individual elements of a represented auditory scene, each of which is a dynamically evolving, connected time-frequency structure that may overlap with other elements. Operating on auditory segments from the segment formation stage, both simultaneous organization and sequential organization will be incorporated. For simultaneous organization, grouping will be based on periodicity, location, onset and offset analyses, while for sequential organization grouping will be based on pitch, spectral, and location continuities. In particular, two pitch maps corresponding to two ears and one location map will be computed for auditory organization. All of the employed grouping cues are consistent with perceptual principles of auditory scene analysis. These cues guide the connectivity of neural oscillator networks, which perform grouping and segregation of auditory segments. The proposed system will be evaluated using real recordings of speech and interfering sounds, where speech can be both voiced and unvoiced. The success of the system will be quantitatively assessed using two measures: changes in signal-to-noise ratio and speech recognition rate. This project is expected to make significant contributions to automatic speech recognition in unconstrained environments.

一个典型的听觉场景包含多个同时发生的事件，听觉神经系统的一个显着的壮举是它能够从同一事件中分离声音混合物并将声能分组。听觉感知的这个基本过程被称为听觉场景分析。在听觉场景分析中特别重要的是将语音与干扰声音分离，或语音分离。语音分离一直是听觉工程和语音技术中一个尚未解决的问题。在这个项目中，P1试图开发一个基于动态的系统，使用感知和神经原理进行语音分离。听觉分组将基于振荡相关性，由此神经振荡器的相位编码听觉特征的结合。调查将包括后续阶段的计算，从耳蜗过滤和毛细胞转导组成的模拟听觉外周开始。通过计算滤波器通道的自相关和互相关，将形成中间级表示。片段形成的阶段然后创建所表示的听觉场景的各个元素，每个元素是动态演变的、连接的时间-频率结构，其可以与其他元素重叠。从段形成阶段开始对听觉段进行操作，同时组织和顺序组织都将被纳入。对于同时组织，分组将基于周期性、位置、起始和偏移分析，而对于顺序组织，分组将基于音高、频谱和位置连续性。特别地，将计算对应于两个耳朵的两个音高图和一个位置图以用于听觉组织。所有的分组线索都符合听觉场景分析的知觉原则。这些线索指导神经振荡器网络的连接，神经振荡器网络执行听觉片段的分组和分离。所提出的系统将使用语音和干扰声音的真实的记录进行评估，其中语音可以是有声的和无声的。该系统的成功将使用两种措施进行定量评估：信噪比和语音识别率的变化。该项目有望为无约束环境下的自动语音识别做出重大贡献。