2 research outputs found
Deep Multi-task Network for Delay Estimation and Echo Cancellation
Echo path delay (or ref-delay) estimation is a big challenge in acoustic echo
cancellation. Different devices may introduce various ref-delay in practice.
Ref-delay inconsistency slows down the convergence of adaptive filters, and
also degrades the performance of deep learning models due to 'unseen'
ref-delays in the training set. In this paper, a multi-task network is proposed
to address both ref-delay estimation and echo cancellation tasks. The proposed
architecture consists of two convolutional recurrent networks (CRNNs) to
estimate the echo and enhanced signals separately, as well as a fully-connected
(FC) network to estimate the echo path delay. Echo signal is first predicted,
and then is combined with reference signal together for delay estimation. At
the end, delay compensated reference and microphone signals are used to predict
the enhanced target signal. Experimental results suggest that the proposed
method makes reliable delay estimation and outperforms the existing
state-of-the-art solutions in inconsistent echo path delay scenarios, in terms
of echo return loss enhancement (ERLE) and perceptual evaluation of speech
quality (PESQ). Furthermore, a data augmentation method is studied to evaluate
the model performance on different portion of synthetical data with
artificially introduced ref-delay.Comment: Submitted to ICASSP 202
Textual Echo Cancellation
In this paper, we propose Textual Echo Cancellation (TEC) - a framework for
cancelling the text-to-speech (TTS) playback echo from overlapping speech
recordings. Such a system can largely improve speech recognition performance
and user experience for intelligent devices such as smart speakers, as the user
can talk to the device while the device is still playing the TTS signal
responding to the previous query. We implement this system by using a novel
sequence-to-sequence model with multi-source attention that takes both the
microphone mixture signal and source text of the TTS playback as inputs, and
predicts the enhanced audio. Experiments show that the textual information of
the TTS playback is critical to enhancement performance. Besides, the text
sequence is much smaller in size compared with the raw acoustic signal of the
TTS playback, and can be immediately transmitted to the device or ASR server
even before the playback is synthesized. Therefore, our proposed approach
effectively reduces Internet communication and latency compared with
alternative approaches such as acoustic echo cancellation (AEC)