基于稀疏编码的鲁棒说话人识别

目前的说话人识别系统在噪声环境下性能将急剧下降,为了解决这一问题,提出一种新的基于稀疏编码的说话人识别方法。该方法用一个通用背景字典(Universal Background Dictionary,UBD)刻画说话人语音的共性,并为每个说话人和环境噪声训练相应的字典来刻画说话人和环境的特殊变化。这些字典拼接成一个大字典,然后将待测试语音稀疏分解在这个大字典上以实现说话人识别。为了提高说话人字典的区分能力,通过从说话人字典中移除与通用背景字典原子相似的原子来优化说话人字典。为了跟踪变化的噪声,采用带噪声的语音在线更新噪声字典。在各种噪声条件下的实验表明,所提出的方法在噪声环境下具有较强的鲁棒性。

ROBUST SPEAKER RECOGNITION BASED ON SPARSE CODEING

Speaker recognition suffers severe performance degradation under noisy environments. To solve this problem, we propose a novel method based on morphological component analysis. This method employs a universal background dictionary (UBD) to model common variability of all speakers, a speaker dictionary to model special variability of each speaker and a noise dictionary to model variability of environmental noise. These three dictionaries are concatenated to be a big dictionary, over which test speech is sparsely represented and classified. To improve the discriminability of speaker dictionaries, we optimize the speaker dictionaries by removing speaker atoms which are close to the UBD atoms. To ensure the varied noises can be tracked, we design an algorithm to update the noise dictionary with the noisy speech. We also conduct experiments under various noise conditions and the results show that the proposed method can obviously improve the robustness of speaker recognition under noisy environments.