While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propose DL-based mel-subband spatio-temporal beamformer to perform speech separation in a car environment with reduced computation cost and inference time. As opposed to conventional subband (SB) approaches, our framework uses a mel-scale based subband selection strategy which ensures a fine-grained processing for lower frequencies where most speech formant structure is present, and coarse-grained processing for higher frequencies. In a recursive way, robust frame-level beamforming weights are determined for each speaker location/zone in a car from the estimated subband speech and noise covariance matrices. Furthermore, proposed framework also estimates and suppresses any echoes from the loudspeaker(s) by using the echo reference signals. We compare the performance of our proposed framework to several NB, SB, and full-band (FB) processing techniques in terms of speech quality and recognition metrics. Based on experimental evaluations on simulated and real-world recordings, we find that our proposed framework achieves better separation performance over all SB and FB approaches and achieves performance closer to NB processing techniques while requiring lower computing cost.

翻译：尽管当前基于深度学习（DL）的波束成形技术已被证明在语音分离中有效，但它们通常被设计为独立处理窄带（NB）频率成分，导致计算成本和推理时间较高，难以适用于实际场景。本文提出一种基于DL的梅尔子带时空波束形成器，在降低计算成本和推理时间的同时实现车内环境下的语音分离。与传统的子带（SB）方法不同，我们的框架采用基于梅尔尺度的子带选择策略，确保对低频区域（包含主要语音共振峰结构）进行细粒度处理，而对高频区域进行粗粒度处理。通过递归方式，根据估计的子带语音和噪声协方差矩阵，为车内每个说话人位置/区域确定鲁棒的帧级波束成形权重。此外，本框架还利用回声参考信号估计并抑制来自扬声器（们）的任何回声。我们从语音质量和识别指标两方面，将所提框架的性能与多种窄带、子带及全带（FB）处理技术进行比较。基于模拟和真实场景录音的实验评估表明，所提框架在所有子带和全带方法中实现了更优的分离性能，其性能接近窄带处理技术，同时所需计算成本更低。