Shubo Lv1,2∗††thanks: *: Equal contribution. Lei Xie is the corresponding
author., Xiong Wang2∗, Sining Sun1, Long Ma2, Lei Xie1

# DCCRN-KWS: AN AUDIO BIAS BASED MODEL FOR NOISE ROBUST SMALL-FOOTPRINT
KEYWORD SPOTTING

###### Abstract

Real-world complex acoustic environments especially the ones with a low
signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword
spotting (KWS) system. Inspired by the recent advances of neural speech
enhancement and context bias in speech recognition, we propose a robust audio
context bias based DCCRN-KWS model to address this challenge. We form the
whole architecture as a multi-task learning framework for both denosing and
keyword spotting, where the DCCRN encoder is connected with the KWS model.
Helped with the denoising task, we further introduce an audio context bias
module to leverage the real keyword samples and bias the network to better
discriminate keywords in noisy conditions. Feature merge and complex context
linear modules are also introduced to strength such discrimination and to
effectively leverage contextual information respectively. Experiments on the
internal challenging dataset and the HIMIYA public dataset show that our
DCCRN-KWS system is superior in performance, while ablation study demonstrates
the good design of the whole model.

Index Terms: Speech enhancement, Keyword Spotting, DCCRN-KWS, audio context
bias

##  1 Introduction

Keyword Spotting (KWS) is a task to detect whether the input speech signal
contains preset keywords. Accordingly, as a typical KWS task, wake-up word
(WuW) detection is the first step in voice interaction between the user and
smart devices [1]. However, there is usually a certain interaction distance
between the user and device, whereas speech signal decay, environmental noise
and room reverberation will seriously affect the performance of the system.

To improve speech quality, a signal enhancement front-end is usually adopted.
When multi-channel signals can be collected from a microphone array,
beamforming or multi-channel signal enhancement technologies can be adopted.
Traditional signal processing based speech enhancement methods usually apply a
spectral suppression gain (or filter) to the noisy signal under the
statistical signal processing theory [2]. With the help of deep learning (DL),
speech enhancement has been recently formulated as a supervised learning
problem and has become the mainstream because of their strong noise reduction
abilities (especially for non-stationary noise) learned from simulated clean-
noisy speech pairs [3, 4]. More recently, advanced network structures such as
SDD-Net [5] and DCCRN [6], which explicitly model the complex spectrum of
speech with particularly designed optimization metrics, have shown outstanding
performance in recent noise suppression challenges [7, 8].

The advances of neural speech enhancement have triggered its downstream
application in speech recognition [9]. The front-end speech enhancement module
can be optimized independently in prior [10]or jointly with the acoustic model
[11]to improve the noise robustness of ASR systems. In [12], the architecture
of deep complex Unet (DCUnet) [12] is incorporated with a multichannel
acoustic model by multi-task learning. The advantages from front- and back-end
integration can be directly applied to KWS which specifically aims to
recognize predefined keywords [13]. However, specifically for the KWS task,
while considering the integration, we can better leverage the constrained
linguistic and acoustic information as keywords are fixed or limited in
number. In this direction, a neural network based text-dependent speech
enhancement (TDSE) method was proposed in [14] for recovering the original
clean speech signal of specific keyword. In the open-vocabulary KWS approach
in [15], an attention-based text-audio cross-modal matching approach was
proposed, where a denoising loss for the acoustic embedding network is
specifically adopted to improve noise robustness.

Aiming to improve the KWS performance under noisy conditions and make full use
of the prior information of keywords, we propose a novel audio context bias
based front-end and back-end integrated KWS model. Inspired by [12], we
cascade the DCCRN encoder with a dilated temporal convolution (DTC) based [16]
KWS model, resulting in the DCCRN-KWS model learned under multi-task learning
framework. To further bias the model to learn specific keywords, inspired by
the advances in context bias in ASR [17], we further introduce an audio bias
encoder which extracts specific keyword embedding from keyword audio samples.
The keyword embedding, subsequently concatenated with the DCCRN encoder
output, is fed into the KWS module. Based on the observation that each DCCRN
encoder layer output has clear discrimination between keywords and non-
keywords, we further introduce a feature merge module to aggregate the outputs
from each layers to further strength such discrimination. Finally we propose a
complex context linear module to better integrate the bias embedding with the
DCCRN encoder output, improving KWS performance further and avoiding model
complexity explosion. Experiments on two challenging datasets clearly show the
advantages of the proposed approach with superior performance and reasonable
complexity. Ablation studies further demonstrate the good design of different
modules.

##  2 Audio Context Bias for DCCRN-KWS

In this section, we first introduce our back-end KWS module and then detail
the proposed DCCRN-KWS architecture, followed by the design of audio context
bias module, feature merge module and complex context linear module
respectively.

###  2.1 Architecture of KWS module

As shown in Fig. 1, our KWS module consists of multiple Dilated Temporal
Convolutional (DTC) blocks, which has achieved impressive performance recently
[16]. In detail, a dilated depthwise 1D-convolution layer is first used to
obtain temporal context, followed by two layers of pointwise convolution to
integrate the latent features from different channels. Finally, we use a fully
connected (FC) layer with softmax function to estimate the posterior
probability of the keyword.

Figure 1: Architecture of KWS module.

###  2.2 Network structure of the proposed DCCRN-KWS

We choose DCCRN [6] as our front-end speech enhancement model. Shaped with an
encoder-decoder Unet structure, deep complex convolution recurrent network was
updated from the CRN network by introducing complex convolution and LSTM,
leading to superior performance in noise suppression. A straightforward way to
unify the DCCRN model with the KWS model is to directly compose them in a
cascaded structure. In this way, we expect the DCCRN front-end to learn the
latent representation that benefits KWS without signal supervision on
enhancement and fully depends on the optimization of the KWS back-end.
However, besides the increased complexity of the whole model, previous work
[12] has shown that this direct composition framework is not as good as the
multi-task learning (MTL) framework with two explicit optimization targets.
Therefore, we shape our architecture in Fig. 2 in an MTL manner [12], where
the main task is keyword spotting and the auxiliary task is speech
enhancement. Specifically, we make the DCCRN encoder shared by the enhancement
decoder and the KWS network in order to extract the latent representation of
the input signals that can benefit both tasks. The whole architecture is
optimized during training and the speech enhancement part is simply discarded
during inference. Based on this architecture, we introduce several important
modules, including audio context bias, feature merge and complex context
linear, detailed in the following subsections.

Figure 2: Audio Context Bias based DCCRN-KWS architecture.

###  2.3 Audio Context Bias

Inspired by the context bias strategy in ASR [17], we introduce a novel audio
context bias module into our DCCRN-KWS model. Different from extracting text
embedding in [15], we apply an audio encoder to extract keyword audio
embedding directly. The main reason is twofold. First, measuring audio-audio
similarity is more straightforward than audio-text similarity; text bias is
more flexible for customizing keyword but it is not the purpose of this paper.
Second, such audio bias can better leverage real audio samples, further helped
with the speech enhancement task to improve robustness in keyword
discrimination in noisy conditions.

In details, we first make a keyword audio list which is selected from the
keyword corpus. The list can be fixed (once chosen from the corpus, it is not
changed anymore) or varied (randomly selected from the corpus every time).
Then we feed the keyword samples on the list to an embedding extractor to
extract the bias embedding. Here we adopt ECAPA-TDNN [18] as the embedding
extractor because this network shows superior ability in information
aggregation from short audio clips and leads to SOTA performance in speaker
verification. Finally we average all the bias embedding vectors on the list
and concatenate the average embedding with the last layer output of the DCCRN
encoder. The concatenated final embedding is used as the input of the KWS
module. Please note that when the keyword audio list is fixed, the keyword
bias embedding is fixed accordingly during the inference time. Therefore, in
this case, the additional complexity of the audio context bias module is
negligible at run-time as the embedding extractor is not in use. In our case,
the dimension of the keyword embedding is set to 192 and ECAPA-TDNN is
implemented by SpeechBrain toolkit [19]. The ECAPA-TDNN based embedding
extractor is jointly trained with the whole DCCRN-KWS structure.

###  2.4 Feature Merge

As we adopt the DCCRN encoder output as the feature for KWS, the purpose of
each encoder layer should be analyzed. To this end, we visualize different
encoder layer's output via frame level energy ${m_{t}}$:

$\vspace{-0.3cm}\begin{cases}m_{t}&=\sum_{c}\sum_{f}M_{c,f,t}\\\
M_{c,f,t}&=\sqrt{\Re{(E)}_{c,f,t}^{2}+\Im{(E)}_{c,f,t}^{2}}\par\end{cases}$
(1)

where ${E_{c,f,t}\in\mathbb{R}^{C\times F\times T}}$ denotes each encoder
layer and ${C,F,T}$ denote the channel number, frequency bins, and time frames
respectively. $\Re{(E)}$ and $\Im{(E)}$ represent the real and imaginary part
of the encoder output respectively. Using a keyword sample and a non-keyword
sample as input, we visualize the layer-wise energy in Fig 3. We can see that
the keyword part shows relatively higher energy as compared with the rest
part, and this phenomenon is more salient for higher encoder layers. In
contrast, this phenomenon is not the case for the non-keyword sample, where
the energy distribution has similar pattern for different layers. Whereas, we
can conclude that the DCCRN encoder aims to enhance the energy of the keyword
part which can subsequently benefit the KWS module to better distinguish the
keyword from other audio parts.

Motivated by this phenomenon, we develop a feature merge module to increase
the discrimination between keywords and the others. Specifically, we first
initialize a group of feature merge ratio $w_{i}$, where the last layer's
ratio is 1 and other layers are 0. Then as the dimensionension of the previous
encoder layer is twice than that of the final layer, we downsample the output
of these layers. Finally, we weighted-average the outputs of the encoder
layers to obtain the feature merge output $E^{\prime}$. The process can be
summarized as

$\vspace{-0.3cm}E^{\prime}=\sum_{i}w_{i}\ast\text{downsample}(E_{i})/\sum_{i}|w_{i}|$
(2)

where $w_{i}$ is learnable weights during training, and fixed during
inference. From Fig 3 (d) and (h), we can see that the keyword segment becomes
more salient after feature merge while the non-keyword is almost unchanged.

Figure 3: The energy of different encoder layers and the feature merge output
for a keyword sample (up row) and a non-keyword sample (bottom row).

###  2.5 Complex Context Linear

A common strategy of combining the bias embedding and encoder output is simply
concatenating them together and subsequently going through a fully connected
layer. As we know, contextual information is essential for the KWS task. To
better model context, another strategy is concatenating several history frames
of temporal features of encoder and the bias embedding as the input of the
fully connected layer. However, this simple concatenation will lead to
computational complexity explosion.

In order to better take the encoder context into account and avoid the
complexity explosion, we specifically modify the fully connected layer shown
in Fig 4. We first split the encoder's output into real/imag parts separately
and then concatenate the real/imag parts with bias embedding respectively.
Finally, the context features of current ($t$) and previous frames ($t-1,t-2$)
are combined together as the fully connected layer's input. Similar to the
group convolution, the complexity is almost halved as compared with simple
context concatenation.

Figure 4: Complex context linear module.

###  2.6 Loss Function

We adopt the typical time-domain SI-SNR [20] loss function for the speech
enhancement task, while binary cross entropy (BCE) loss is used as the KWS
loss. The final loss can be formulated as

$\vspace{-0.1cm}\begin{cases}\mathcal{L}_{\textbf{BCE}}&=-y_{i}*ln(y_{i})-(1-y*)ln(1-y_{i})\\\
\mathcal{L}&=\mathcal{L}_{\textbf{SI-
SNR}}+\mathcal{L}_{\textbf{BCE}}\end{cases}\vspace{-0.1cm}$ (3)

where ${y_{i}=H(x_{i};\theta)\in\\{0,1\\}}$ is the posterior predicted by the
KWS model $H$ with parameter $\theta$, ${y*_{i}\in\\{0,1\\}}$ is the ground-
truth class label for frame $i$.

##  3 Experiment

###  3.1 Dataset

We first conduct ablation experiments to prove the effectiveness of each
proposed sub-modules on a large internal training set and task is to detect
the keyword ``ding1-dang2-ding1-dang2`` in Mandarin. Specifically, a 158-hour
keyword-specific speech dataset is used as positive training data and a
139-hour speech dataset collected from Youtube is served as negative training
data. The test is recorded in far-field noisy conditions, where the number of
positive and negative samples are 239 and 12,480 respectively with SNR ranging
from -6 to -2db.

Besides the internal task, we further validate our approach on the publicly
available HIMIA dataset [21] recorded from a high-fidelity microphone with
spoken keywords ``ni2-hao3-mi1-ya4`` in Mandarin. The training set contains 10
hours of keyword samples from 254 speakers and the test set is composed of
3,520 keyword samples from another 44 speakers. As speaker labels are known,
speaker-dependent audio list for keyword bias can be further studied. In this
task, we adopt 90 hours of negative samples selected from the librivox speech
corpus. For testing, besides the above positive testing samples, another 3,600
negative samples are randomly selected from librivox, in total 7,120 audio
samples.

During the training for both tasks, the training data are generated on-the-fly
with SNR ranging from 0 to 15 dB and the image method [22] is used to simulate
RIR with RT60 ranging from 0.05s to 0.95s. For the internal set, the noises
are selected from both an internal noise set and freesound,totally 174 hours.
As for the HIMIYA public set, 56 hours of noises are selected from freesound.
During training, we randomly add 1 to 4 types of noise to each speech
utterance. Particularly, the speakers and noises have no overlap between
training and testing. Furthermore, particularly for the HIMIYA set, we
randomly clip the negative examples to short clips with duration of 1 to 3 s
for both training and testing in order to avoid the model overfitting to
specific time duration since the length of positive examples ranges from 1 to
3 s. To test our model's performance on different SNR conditions, we add noise
to the HIMIYA testing samples under SNR of -5, 0, and 5dB respectively.

###  3.2 Training setup and baselines

For the proposed model, the window length and frame shift are 25ms and 10ms
respectively while the STFT length is 512. Our model is a casual one without
considering the future frames. For both datasets, all models are trained for
17,500 iterations. And we use 8 NVIDIA V100 32GB GPUs for internal dataset
while 1 for the public one. Adam optimizer is used with a Noam-based scheduler
[23], where the Noam factor is 5 and the warm-up step is 1,000. During
training, the number of the audio bias list is set to 50 and the input
dimensions of the KWS module are 128. Furthermore, the fully connected layer
between the final encoder output and the KWS module for DCCRN-KWS (w/o audio
context bias), DCCRN-KWS (w/o complex context linear) and DCCRN-KWS (w complex
context linear) are 1024 $\times$ 128, 1216 $\times$ 128, 608 $\times$ 64
$\times$ 3 separately. For the complex context linear module, the context
number is set to 3 (previous two frames with the current frame). The model
details are described as follows.

KWS module: The number of DTC layers is 16, and the kernel size and dilation
are 5 and 1-2-4-8-1-2-4-8-1 respectively. To achieve streaming inference, all
convolutions are causal ones.

DCCRN: The number of channels for the DCCRN is {16,32,64,
128,256,256}, and the convolution kernel and step are set to (5,2) and (2,1)
respectively. Two 256-dimension LSTM layers are adopted with 256-dimension
project layer. Each encoder/decoder module handles the current frame and one
previous frame.

During training, we select up to 10 frames around the last frame of a keyword
audio clip as positive training samples and assign 1 to them, while other
frames in the keyword audio clip are discarded as ambiguous and are not used
in training. For negative training utterances, all frames are regarded as
negative training samples and assigned to 0 accordingly.

###  3.3 Experimental results and discussion

####  3.3.1 Results on internal dataset

As presented in Fig 5, ablation studies are conducted to evaluate the
effectiveness of different model components of our system, including a) DCCRN
module, b) Audio context bias module (Fix / not Fix), c) Feature merge module
(FM), and d) Complex context linear module (CCL). In order to show the
effectiveness of audio context bias, we replace the bias embedding with a
learnable bias embedding as a sanity check. For this learnable bias embedding,
we first initialize a group of parameters as the bias embedding, then we send
the bias embedding into the DCCRN-KWS module to update the value with the
model training. The performance is measured by plotting the receiver operating
curve (ROC), which calculates the false reject (FR) rate per false alarm (FA)
rate. The lower the FR per FA rate, the better the system.

Figure 5: Results of comparison models and ablation of the proposed model on
internal dataset. Here FM denotes Feature Merge module and CCL denotes Complex
Context Linear module.

It can be seen from Fig 5(a) that after applying the DCCRN module, the
performance becomes obviously better. This indicates that the use of an
explicit speech enhancement module under multi-task learning framework is
helpful to assist the KWS module to improve performance. From Fig 5(b), adding
audio context bias module yields a lower false reject rate when the false
alarm rate kept the same with the system without the bias module, which shows
the effectiveness of keyword audio embedding. We notice that fixed keyword
audio list (Fix) performs better than varied list during training. This is
probably because the model can be more easily trained and optimized better
with a fixed list during training.

By comparing the learnable embedding with the embedding extracted by ECAPA-
TDNN, we can see that the embedding extracted by keyword audio list is more
effective than the learnable embedding. We can explain this phenomenon from
several aspects. First, the embedding extracted from keyword audio contains
more structure information of keywords than the learnable embedding. Second,
we extract the bias embedding from real audio samples, which is easy to
calculate the similarity with the DCCRN-KWS model compared with the learnable
embedding. Furthermore, as shown in Fig 5(c), when the feature merge module is
applied, the performance is further improved. This is because the feature
merge module can emphasize the discrimination of the keyword part as plotted
in Fig 3, which can help the KWS module to distinguish the keyword from
others. Finally, when the complex context linear module is adopted, our system
achieves the best performance. This indicates that combining the context
information and bias embedding can better discriminate the keyword. In
summary, the overall comparison on all methods is shown in Fig 5(d).

Figure 6: Results of various models and ablation of the proposed model on
public HIMIYA dataset.

####  3.3.2 Results on HIMIYA dataset

We further evaluate our models trained on the HIMIYA dataset. Fig 6 shows the
KWS performance measured by wake-up accuracy under the setup that up to one
time false alarm triggered in 10 hours’ exposure to continuous noisy speech.
Whether it is low-snr scenario, high-snr scenario, or even clean scenario, our
contributions show their clear effectiveness according to the results. Please
note that when speaker-dependent audio samples are applied as the audio list,
the performance is better than the use of speaker-independent audio list. This
is because for the speaker-dependent audio list, the extracted embedding
contains both keyword information and the speaker information. Furthermore, we
notice that the improvements achieved from the proposed methods are less on
the HIMIYA dataset than the internal dataset. The main reason is that the
duration of the enrollment audio in the HIMIYA dataset is relatively shorter
than that in the internal dataset, while empirically a longer bias keyword
will lead to a more robust keyword embedding.

###  3.4 Inference Time

As shown in Table LABEL:tab:rtf, we evaluate the RTF and CPU usage on RK3326
<EMAIL_ADDRESS>low resource platform. Inference precision is set at float32
with ONNX runtime engine. We also implement a comparison system, named as
DCCRN-KWS-joint-train in Table LABEL:tab:rtf. In this comparison system, we
jointly train a cascaded DCCRN-KWS system with the two losses, where the
enhanced speech by DCCRN is fed to the DTC-basded KWS system. We can see that
the CPU usage and RTF for this system are not acceptable. In contrast, the
proposed DCCRN-KWS has reasonable CPU usage and RTF. Importantly, based on the
DCCRN-KWS framework, the components we further proposed yield only small
increase on RTF.

Table 1: RTF and CPU usage on low resource platform. Model | CPU Usage (%) | RTF
---|---|---
KWS | 16 | 0.27
DCCRN-KWS | 35 | 0.67
\+ AudioBias | 36 | 0.69
\+ FM | 36 | 0.70
\+ CCL | 38 | 0.72
DCCRN-KWS-joint-train | 94 | 2.85

##  4 Conclusion

In this paper, we introduce a front-end and back-end integration framework for
KWS in noisy conditions. Specifically, we shape the integration in a multi-
task learning manner, while DCCRN is adopted for speech enhancement and its
encoder output is coupled with the KWS model for keyword spotting. Based on
this MTL architecture, we particularly propose an audio bias module which aims
to better learn discrimination between keyword and non-keyword. Feature merge
and complex context linear modules are also introduced to strengthen such
discrimination and to effectively leverage contextual information
respectively. Experiments on two datasets show the effectiveness of the
proposed approach.

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