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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step
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Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step

机译：训练嘈杂的单通道语音分离与嘈杂的oracle来源：一个巨大的差距和一小步

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摘要

As the performance of single-channel speech separation systems has improved, there has been a desire to move to more challenging conditions than the clean, near-field speech that initial systems were developed on. When training deep learning separation models, a need for ground truth leads to training on synthetic mixtures. As such, training in noisy conditions requires either using noise synthetically added to clean speech, preventing the use of in-domain data for a noisy-condition task, or training using mixtures of noisy speech, requiring the network to additionally separate the noise. We demonstrate the relative inseparability of noise and that this noisy speech paradigm leads to significant degradation of system performance. We also propose an SI-SDR–inspired training objective that tries to exploit the inseparability of noise to implicitly partition the signal and discount noise separation errors, enabling the training of better separation systems with noisy oracle sources.
机译：随着单通道语音分离系统的性能提高，渴望比清洁，近场演讲更具挑战性的条件，即初始系统开发的。 在培训深度学习分离模型时，需要对原始事件导致合成混合物的培训。 因此，嘈杂条件的培训需要使用合成且良好地添加到清洁语音的噪声，从而防止使用域内数据的噪声条件任务，或使用嘈杂的语音混合物进行培训，要求网络另外分离噪声。 我们展示了噪音的相对不可分割性，这种嘈杂的语音范例导致系统性能的显着降低。 我们还提出了一个SI-SDR激发的培训目标，试图利用噪声的不可分割性来隐式分区信号和折扣噪声分离错误，从而能够培训具有嘈杂的Oracle来源的更好的分离系统。

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