96 research outputs found
Cross-domain Voice Activity Detection with Self-Supervised Representations
Voice Activity Detection (VAD) aims at detecting speech segments on an audio
signal, which is a necessary first step for many today's speech based
applications. Current state-of-the-art methods focus on training a neural
network exploiting features directly contained in the acoustics, such as Mel
Filter Banks (MFBs). Such methods therefore require an extra normalisation step
to adapt to a new domain where the acoustics is impacted, which can be simply
due to a change of speaker, microphone, or environment. In addition, this
normalisation step is usually a rather rudimentary method that has certain
limitations, such as being highly susceptible to the amount of data available
for the new domain. Here, we exploited the crowd-sourced Common Voice (CV)
corpus to show that representations based on Self-Supervised Learning (SSL) can
adapt well to different domains, because they are computed with contextualised
representations of speech across multiple domains. SSL representations also
achieve better results than systems based on hand-crafted representations
(MFBs), and off-the-shelf VADs, with significant improvement in cross-domain
settings
Improving speech recognition by revising gated recurrent units
Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-term dependencies and robustness to vanishing gradients.
Nevertheless, LSTMs have a rather complex design with three multiplicative
gates, that might impair their efficient implementation. An attempt to simplify
LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just
two multiplicative gates.
This paper builds on these efforts by further revising GRUs and proposing a
simplified architecture potentially more suitable for speech recognition. The
contribution of this work is two-fold. First, we suggest to remove the reset
gate in the GRU design, resulting in a more efficient single-gate architecture.
Second, we propose to replace tanh with ReLU activations in the state update
equations. Results show that, in our implementation, the revised architecture
reduces the per-epoch training time with more than 30% and consistently
improves recognition performance across different tasks, input features, and
noisy conditions when compared to a standard GRU
ViVoVAD: a Voice Activity Detection Tool based on Recurrent Neural Networks
Voice Activity Detection (VAD) aims to distinguishcorrectly those audio segments containing humanspeech. In this paper we present our latest approachto the VAD task that relies on the modellingcapabilities of Bidirectional Long Short TermMemory (BLSTM) layers to classify every frame inan audio signal as speech or non-speec
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
Recent advances in Voice Activity Detection (VAD) are driven by artificial
and Recurrent Neural Networks (RNNs), however, using a VAD system in
battery-operated devices requires further power efficiency. This can be
achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs)
to perform inference at very low energy consumption. Spiking networks are
characterized by their ability to process information efficiently, in a sparse
cascade of binary events in time called spikes. However, a big performance gap
separates artificial from spiking networks, mostly due to a lack of powerful
SNN training algorithms. To overcome this problem we exploit an SNN model that
can be recast into an RNN-like model and trained with known deep learning
techniques. We describe an SNN training procedure that achieves low spiking
activity and pruning algorithms to remove 85% of the network connections with
no performance loss. The model achieves state-of-the-art performance with a
fraction of power consumption comparing to other methods.Comment: 5 pages, 2 figures, 2 table
Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation
Singing voice detection is the task to identify the frames which contain the
singer vocal or not. It has been one of the main components in music
information retrieval (MIR), which can be applicable to melody extraction,
artist recognition, and music discovery in popular music. Although there are
several methods which have been proposed, a more robust and more complete
system is desired to improve the detection performance. In this paper, our
motivation is to provide an extensive comparison in different stages of singing
voice detection. Based on the analysis a novel method was proposed to build a
more efficiently singing voice detection system. In the proposed system, there
are main three parts. The first is a pre-process of singing voice separation to
extract the vocal without the music. The improvements of several singing voice
separation methods were compared to decide the best one which is integrated to
singing voice detection system. And the second is a deep neural network based
classifier to identify the given frames. Different deep models for
classification were also compared. The last one is a post-process to filter out
the anomaly frame on the prediction result of the classifier. The median filter
and Hidden Markov Model (HMM) based filter as the post process were compared.
Through the step by step module extension, the different methods were compared
and analyzed. Finally, classification performance on two public datasets
indicates that the proposed approach which based on the Long-term Recurrent
Convolutional Networks (LRCN) model is a promising alternative.Comment: 15 page
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning
is Automatic Speech Recognition (ASR). Despite the great achievements of the
past decades, however, a natural and robust human-machine speech interaction
still appears to be out of reach, especially in challenging environments
characterized by significant noise and reverberation. To improve robustness,
modern speech recognizers often employ acoustic models based on Recurrent
Neural Networks (RNNs), that are naturally able to exploit large time contexts
and long-term speech modulations. It is thus of great interest to continue the
study of proper techniques for improving the effectiveness of RNNs in
processing speech signals.
In this paper, we revise one of the most popular RNN models, namely Gated
Recurrent Units (GRUs), and propose a simplified architecture that turned out
to be very effective for ASR. The contribution of this work is two-fold: First,
we analyze the role played by the reset gate, showing that a significant
redundancy with the update gate occurs. As a result, we propose to remove the
former from the GRU design, leading to a more efficient and compact single-gate
model. Second, we propose to replace hyperbolic tangent with ReLU activations.
This variation couples well with batch normalization and could help the model
learn long-term dependencies without numerical issues.
Results show that the proposed architecture, called Light GRU (Li-GRU), not
only reduces the per-epoch training time by more than 30% over a standard GRU,
but also consistently improves the recognition accuracy across different tasks,
input features, noisy conditions, as well as across different ASR paradigms,
ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.Comment: Copyright 2018 IEE
Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence
Personal voice activity detection has received increased attention due to the
growing popularity of personal mobile devices and smart speakers. PVAD is often
an integral element to speech enhancement and recognition for these
applications in which lightweight signal processing is only enabled for the
target user. However, in real-world scenarios, the detection performance may
degrade because of competing speakers, background noise, and reverberation. To
address this problem, we proposed to use equivalent rectangular bandwidth
ERB-scaled spatial coherence as the input feature to train an array
configuration-agnostic PVAD network. Whereas the network model requires only
112k parameters, it exhibits excellent detection performance and robustness in
adverse acoustic conditions. Notably, the proposed ARCA-PVAD system is scalable
to array configurations. Experimental results have demonstrated the superior
performance achieved by the proposed ARCA-PVAD system over a baseline in terms
of the area under receiver operating characteristic curve and equal error rate.Comment: Accepted by INTER-NOISE 2023. arXiv admin note: text overlap with
arXiv:2211.0874
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