321 research outputs found
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model
Far-field speech recognition is a challenging task that conventionally uses
signal processing beamforming to attack noise and interference problem. But the
performance has been found usually limited due to heavy reliance on
environmental assumption. In this paper, we propose a unified multichannel
far-field speech recognition system that combines the neural beamforming and
transformer-based Listen, Spell, Attend (LAS) speech recognition system, which
extends the end-to-end speech recognition system further to include speech
enhancement. Such framework is then jointly trained to optimize the final
objective of interest. Specifically, factored complex linear projection (fCLP)
has been adopted to form the neural beamforming. Several pooling strategies to
combine look directions are then compared in order to find the optimal
approach. Moreover, information of the source direction is also integrated in
the beamforming to explore the usefulness of source direction as a prior, which
is usually available especially in multi-modality scenario. Experiments on
different microphone array geometry are conducted to evaluate the robustness
against spacing variance of microphone array. Large in-house databases are used
to evaluate the effectiveness of the proposed framework and the proposed method
achieve 19.26\% improvement when compared with a strong baseline
Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation
Recently, frequency domain all-neural beamforming methods have achieved
remarkable progress for multichannel speech separation. In parallel, the
integration of time domain network structure and beamforming also gains
significant attention. This study proposes a novel all-neural beamforming
method in time domain and makes an attempt to unify the all-neural beamforming
pipelines for time domain and frequency domain multichannel speech separation.
The proposed model consists of two modules: separation and beamforming. Both
modules perform temporal-spectral-spatial modeling and are trained from
end-to-end using a joint loss function. The novelty of this study lies in two
folds. Firstly, a time domain directional feature conditioned on the direction
of the target speaker is proposed, which can be jointly optimized within the
time domain architecture to enhance target signal estimation. Secondly, an
all-neural beamforming network in time domain is designed to refine the
pre-separated results. This module features with parametric time-variant
beamforming coefficient estimation, without explicitly following the derivation
of optimal filters that may lead to an upper bound. The proposed method is
evaluated on simulated reverberant overlapped speech data derived from the
AISHELL-1 corpus. Experimental results demonstrate significant performance
improvements over frequency domain state-of-the-arts, ideal magnitude masks and
existing time domain neural beamforming methods
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
In this work, we investigated the teacher-student training paradigm to train
a fully learnable multi-channel acoustic model for far-field automatic speech
recognition (ASR). Using a large offline teacher model trained on beamformed
audio, we trained a simpler multi-channel student acoustic model used in the
speech recognition system. For the student, both multi-channel feature
extraction layers and the higher classification layers were jointly trained
using the logits from the teacher model. In our experiments, compared to a
baseline model trained on about 600 hours of transcribed data, a relative
word-error rate (WER) reduction of about 27.3% was achieved when using an
additional 1800 hours of untranscribed data. We also investigated the benefit
of pre-training the multi-channel front end to output the beamformed log-mel
filter bank energies (LFBE) using L2 loss. We find that pre-training improves
the word error rate by 10.7% when compared to a multi-channel model directly
initialized with a beamformer and mel-filter bank coefficients for the front
end. Finally, combining pre-training and teacher-student training produces a
WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
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