Cleanformer：一种用于智能音箱中自动语音识别（ASR）的多通道阵列配置不变性神经增强前端
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers
AI总结：
本研究提出适配不同麦克风阵列、无需重训的流式多通道ASR神经增强前端Cleanformer，基于conformer架构与自注意力生成时频掩码，在-6dB信噪比下相对WER降低约80%，性能优于理想导向波束形成方法。
摘要：
本研究提出了Cleanformer，这是一种面向自动语音识别（ASR）的流式多通道神经增强前端。该模型采用基于conformer的架构，以单通道原始信号与增强信号作为输入，利用自注意力机制推导时频掩码。其中增强输入由名为Speech Cleaner的多通道自适应噪声抵消算法生成，该算法利用噪声上下文推导其滤波器抽头。模型将得到的时频掩码施加于带噪输入，为ASR生成增强后的输出特征。研究采用模拟数据集与重录数据集，在含语音类噪声与非语音类噪声的场景下开展了详细评估，结果显示，搭配大规模前沿ASR模型使用时，词错误率（WER）显著降低；同时该方法的性能明显优于采用理想导向的波束形成增强方法。该增强模型对麦克风数量与阵列配置不敏感，因此无需重新训练即可适配不同的麦克风阵列。实验证实，麦克风数量最多增加到4个时性能会持续提升，每新增一个麦克风带来的边际收益会逐渐递减。具体而言，在信噪比为-6dB的条件下，两种噪声场景下的WER相对提升幅度均约为80%。
英文摘要：
This work introduces the Cleanformer, a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR). This model has a conformer-based architecture which takes as inputs a single channel each of raw and enhanced signals, and uses self-attention to derive a time-frequency mask. The enhanced input is generated by a multichannel adaptive noise cancellation algorithm known as Speech Cleaner, which makes use of noise context to derive its filter taps. The time-frequency mask is applied to the noisy input to produce enhanced output features for ASR. Detailed evaluations are presented with simulated and re-recorded datasets in speech-based and non-speech-based noise that show significant reduction in word error rate (WER) when using a large-scale state-of-the-art ASR model. It also will be shown to significantly outperform enhancement using a beamformer with ideal steering. The enhancement model is agnostic of the number of microphones and array configuration and, therefore, can be used with different microphone arrays without the need for retraining. It is demonstrated that performance improves with more microphones, up to 4, with each additional microphone providing a smaller marginal benefit. Specifically, for an SNR of -6dB, relative WER improvements of about 80\% are shown in both noise conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
