Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 218–223
DOI: https://doi.org/10.2991/jrnal.k.210922.013; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Research on LSTM+Attention Model of
Infant Cry Classification
Tianye Jian, Yizhun Peng*, Wanlong Peng, Zhou Yang
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China

ARTICLE INFOABSTRACT

Article History

According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and theinfant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established.
The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in theoriginal speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention
mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to theself-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs
database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84%
and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training timeis shorter and the speech emotion recognition rate of baby is higher.

Received 16 November 2020
Accepted 28 July 2021
Keywords
Children’s emotionaltime–sequence relationshipframe-level speech feature
deep attention gate
long- and short-term memory(LSTM)© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
An important part of affective computing includes children’s emotion recognition [1]. Children are far less able than adults to act
rationally in emotional outbursts and in response to different emotions, which can lead to emotional disorders if children are notable to act rationally and are directed in a timely manner, which
can lead to anxiety disorders and other mental health problems.
Therefore, it is of great significance to use appropriate algorithms
to judge children’s emotions.
Researchers have conducted in-depth research on children’s
emotion recognition using methods such as acoustic features,
machine learning, and deep learning [2]. It is proposed to use
Support Vector Machine (SVM) and Convolutional Neural
Network (CNN) to construct a system for detecting children’s
secondary emotional states [3] real-time emotional state of children is defined by multi-agent based interaction system [4] toestablish the children’s dual-modal emotion database and use thedual-modal emotion recognition method to measure the proportion of children’s emotion contribution, and to point out that
infants’ (or young children’s) emotion is more difficult to judge
than older children’s, babies usually cry to express their needs
to their parents or guardians [5] the Mel-Frequency Cepstrum
Coefficients (MFCC) of infant cries were extracted and classified
based on Hidden Markov Model (HMM) to identify whether the
infant cries were healthy or not [6]. The Spectrogram was used
Corresponding author. Email: pengyizhun@tust.edu.cn*

as the feature vector, and the CNN was selected as the classification model to classify and recognize the crying of infants in pain,
hunger and sleepiness [7]. The SVM was used as the classifier toclassify the crying sounds of infants in the condition of hunger,
pain and sleepiness, and the recognition effect was better thanthat of the SVM.
The above algorithms have been successfully applied in the fieldof children’s emotion recognition, but traditional machine learningalgorithms, as well as autoencoders and CNNs in deep learning,
can only accept fixed-dimensional data as input. This is in contradiction with the fact that the effective length of speech is constantly
changing. To solve this problem, reference extracts emotion-related
features (hereinafter referred to as frame-level features) from shortterm speech frames, and applies static statistical functions (such asmean, variance, maximum, linear regression coefficient) to framelevel features, finally, the feature vectors with fixed dimensions
are formed to represent the features of the frame speech [8–10].
Although this method solves the problem of model input, the time
sequence information of the original speech is lost through thestatistical analysis of the speech features.
We propose an improved Long- and Short-Term Memory (LSTM)
based children’s speech emotion recognition model. Based on the
LSTM network structure, the frame-level speech features are substituted for the traditional statistical features, in order to obtainbetter recognition performance, attention gates were used toreplace the traditional forgetting gates and input gates, and to construct the deep attention gates by weighting attention in multiple
cellular states.

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2. RELATED WORK
2.1. LSTM Network
Long- and short-term memory network is mainly used to process
sequence information with a large time difference. It is a variantof the Recurrent Neural Network (RNN). The LSTM network
can solve the problem that the long-term information is difficult
to store because of the gradient disappearance of RNN in thereverse propagation [11–13]. The LSTM network has been successfully applied in Natural Language Processing (NLP) [14–16]. To
enhance the ability of LSTM network to process data in specifictasks, the researchers further optimized the internal structure of
LSTM network. The fusion of the input gate and the forgetting gateof the LSTM network by the Gated Recurrent Unit (GRU) reduces
the model parameters [17], the performance of LSTM network is
better than that of GRU [18] in all machine translation tasks [19].
By using CONVLSTM network structure, the computing methodof gate structure of LSTM is improved from Matrix multiplication
to convolution [20]. Infinite Impulse Response (IIR) Filter Memory
block of RNN is improved to Finite Impulse Response (FIR) Filter
Memory block by Feedforward Sequential Memory Network(FSMN) [21], but FSMN usually needs to stack very deep layers,
so FSMN has delay compared with one-way LSTM network [22].
Advanced LSTM network, which uses attention mechanism toweight multiple cell states, can be effectively used for emotion recognition. However, Xie et al. [23] indicates that this method does
not change the gate structure in the LSTM network and requires
more training time. In addition, researchers are exploring how tostack LSTM structures to achieve more reliable emotion recognition [24]. End-to-end emotion recognition is achieved by extracting multi-channel speech features from a 6s-long speech waveform
via a CNN as input to the LSTM Network [25]. In this paper, 1280
kinds of abstract features were extracted from 6s-long speech waveform by CNN, and then fused with facial features as input of LSTM
network [26].
The formula used in the traditional LSTM network is as follows:		 ft = r (W f ⋅ [ht −1 , xt ] + b f ) (1)

		 it = r (Wi ⋅ [ht −1 , xt ] + bi ) (2)	    Ct = tan h(WC × [ht -1 , xt ] + bc ) (3)	    Ct = ft × Ct -1 + it × Ct 

(4)	    Ot = r (wo × [ht -1 , xt ] + bo ) (5)		 ht = ot ⋅ tanh (Ct ) (6)cell state C and hidden layer output h) is only based on the external
input and the previous unit state and hidden layer output. Before
the attention mechanism is put forward, if each moment is considered several times before, it will lead to too much informationand the loss of important information, and increase the calculation amount and lead to the gradient explosion [27]. However, theinformation of cell state at t-time is not only related to the information at t − 1, but also closely related to the information at t − 2,which is selectively forgotten at t − 1. In this paper, the concept ofdeep forgetting gate is proposed and the corresponding input gate
is designed.
The deep forgetting gate looks not only at the information about
the state of the cell at the last moment (depth length = 1), but also
at the information about the state of the cell at the time t − 2, t − 3,…, t − n (depth length = n), which constructs the Deep-AttentionLSTM Cell structure, as shown in Figure 1.
It is worth noting that the introduction of “depth” will lead to anincrease in training time. This is because in addition to forward
increasing the number of attention gates in the loop for computing
the state of multiple cells, reverse propagation also increases thenumber of chain derivations [28,29]. From the point of view of theparameters of the model, although the depth will cause the increaseof the training time, it will not cause the increase of the parametersof the model because the attention gate weights of each layer are
shared by V and W.
In this paper, the aim of depth is to improve the performanceof speech emotion recognition [30]. To study the performance
improvement, the following experiments were carried out:
In experiment 1 investigated the effect of depth performance onchildren’s emotion recognition rate [31]. The attention-gate-based
LSTM model (hereafter referred to as the attention-gate LSTM
model) at depths 1–3 is used for comparison.
In experiment 2, the effects of decreasing the number of parameters and training time on speech emotion recognition performance
were investigated [32]. The attention gate LSTM model with depth 1
was compared with the traditional GRU model and LSTM model.

2.3. Training Framework
The training framework for the deep attention gate LSTM modelis shown in Figure 2. Among them, LSTM0 represents the LSTM
model of the first-level deep attentional gate, and LSTM1 represents the LSTM model of the second-level deep attentional gate.
Xt extracts speech features [8–10] from INTERSPEECH in frame t
after framing and windowing, ht and Ct are the hidden layer output

s is the sigmoid activation function, ht−1 is the hidden layer output
at t − 1, xt is the input at t, Ct−1 is the cell state at t − 1, Ct is the candidate value of the cell state at t, ft, it and ot are respectively forgettinggate, input gate and output gate.

2.2. Deep Attention Gate
Time series information will increase over time. Therefore, the calculation of the LSTM model at a certain time (that is, update the219

Figure 1 | Deep-attention-LSTM cell internal structure sketch.

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T. Jian et al. / Journal of Robotics, Networking and Artificial Life 8(3) 218–223

activation function, as shown in Table 1. To ensure the validity ofexperimental comparison, the same Corpus and model experimental parameters are identical.

4. ALGORITHM PERFORMANCE ANALYSIS

Figure 2 | Training framework of depth of attention gate LSTM model.

and cell state of its corresponding LSTM model output. As can beseen from Figure 2, the input state of the traditional LSTM modelat time t is (ht−1, Ct−1), while in the training of this paper, the input
state at each time is expanded to (ht−1, {Ct−1, Ct−2, …, Ct−L}), where
L is the depth of the attention gate. The last state of the last layerof LSTM, which contains all the temporal information of the preorder, is input into the subsequent classification network for theidentification of children’s emotions [33–35].

The traditional LSTM model removes redundant information
through forgetting gates, and obtains new information throughinput gates. In this paper, we use the self-attention and the basic
structure of LSTM to do self-attention to the cell state, so as tocompare the forgetting gate and the input gate of LSTM. At thesame time, considering the correlation of time series information, the depth-based self-attention Gates are proposed and compared at the depth of 1–3. Four kinds of models were compared:
Traditional model, LSTM + deepf_1 model, LSTM + deepf_2
model, and LSTM + deepf_3 model. The depth distributionsof these models are 0–3, as shown in Figure 3. As can be seenfrom Figures 3 and 4, after replacing the forgetting door and theTable 1 | Experimental parameters

3. EXPERIMENTAL SETUP AND ANALYSIS

Name ofparameter

3.1. Experimental Setup

Eta

In our experiment, we used two databases of different emotion
representations to verify the effectiveness of the algorithm in thispaper. To study the performance of this algorithm in dealing withother types of emotion recognition problems, and whether reducing the number of parameters can optimize the time or reduce theperformance [36–39], Fau Aibo Children’s affective Corpus, infantcrying affective needs Corpus and Chinese Academy of Sciences’Institute of Automation (CASIA) Chinese affective Corpus wereused to verify the results.

Batch size
Epochs
LSTM cells
Dense layers
Softmax layers
L2

Baby crying needs
corpus parametervaluesFau Aibo

CASIA

1e−3
beta2 = 0.7641500[512, 256]
[256, 128]
[128, 5]1e−4

1e−4
beta2 = 0.91281200[512, 256]
[256, 128]
[128, 5]1e−4

1e−4
beta2 = 0.91281200[512, 256]
[256, 128]
[128, 6]1e−4

3.2. Frame Level Feature Selection
In this experiment, some frame-level features are selected basedon INTERSPEECH’s speech emotion features [39]. In Schuller et
al. [8], 16 Low-Level Descriptors (LLD, zero-crossing rate, rootmean-square Frame Energy, pitch frequency and MFCC 1–12) andtheir difference coefficients are extracted. For each descriptor, 12
statistical functions are calculated, so the total eigenvector has 16 ×
2 × 12 = 384 features. On this basis, the speech emotion characteristics of INTERSPEECH 2010 (IS2010) have been increased to 38
kinds of LLD, so the total feature dimension has been expanded to1582 dimensions. The feature dimension of the speech feature set isincreased to 6373 dimensions.

Figure 3 | The performance of infant crying emotional needs corpus indifferent LSTM models.

3.3. Setting of Experimental Parameters
The original data is divided into training set and test set according to the ratio of 4:1. The experiments all adopt unidirectional
two-layer LSTM stack structure and use one full connection layer
and one Softmax layer as the training model. During the training,a small batch of gradient descent is used and Tanh is used as the

Figure 4 | Performance of different LSTM models using Fau Aibo
children’s emotion corpus database.

T. Jian et al. / Journal of Robotics, Networking and Artificial Life 8(3) 218–223

output door of the traditional LSTM model with the attention
door proposed by using the infant crying affective needs corpus
and Fau Aibo children affective Corpus, the convergence rateof the attention gate LSTM model on the training set and thetest set is much higher than that of the traditional LSTM model,
when Fau Aibo is used in children’s affective Corpus, the traditional LSTM model starts to converge at about 30,000, while theattention gate model starts to converge at about 17,000 when themodel converges, the attention-gate LSTM model outperforms
the traditional LSTM model in the average recognition rate ofchildren’s emotion.

221

From Table 2, it can be seen that the recall rate of attention-gate
LSTM model is better than that of traditional LSTM model exceptthat the items of drowsiness and sleepiness are close to each other
The F1-scores of attention gate LSTM model were better than thoseof traditional LSTM model in five kinds of emotion. In the aspect of
depth, the performance of the attention-gate LSTM model of depth
3 and 2 is close to that of the attention-gate LSTM model of depth
1, except “sad”, the recall rate and F1-score of the other four itemsof the model are better than those of depth 1.
From Table 3, it can be seen that when FAU AIBO is used in children’s affective corpus, the recall rate and F1-score of attentiongate LSTM model are lower than those of traditional LSTM model
except that the class E is lower than that of traditional LSTM model,
and the other four items are better than traditional LSTM model. In
the aspect of depth, the performance of the attentional gate LSTM
model of depth 3 and 2 is close to that of the attentional gate LSTM
model of depth 1, except for class R, the recall rate and F1 score of
the other four items of the attentional gate LSTM model are better
than those of depth 1.

According to the analysis above, the performance of attentiongate LSTM model is improved because it modifies the forgetting gate and input gate of traditional LSTM model, it makes the
LSTM model leave important information by self-attention to thecell state at the last moment, and supplement the unimportant
information as the new input in the corresponding position, thus
improving the performance of the LSTM model. The attentiongate LSTM model introduces the concept of depth so that each
forgetting operation is determined by multiple cell states rather
than one of them.

It should be noted that the sample size of Fau Aibo Children’s affective corpus is uneven, with a maximum of 5376 samples in n category and only 215 samples in P category. From the above analysis,
with the increase of depth, the model can enhance the learning ofa small number of samples. Compared with the traditional LSTM
model, the recall rate of LSTM + deepf_1model was increased by
5.50%, and the F1-score was increased by 5.49%, and the recall rateof LSTM + deepf_2 model was increased by 3.14% when Fau Aibo
was used, the F1-score of LSTM + deepf_3 model was increasedby 1.84%.

To compare the performance of different models in the test set foreach kind of emotion, the performance index of the model with thehighest recognition rate from the beginning of training to the endof the test set was Quantitative analysis, the performance indicators
obtained from the infant crying affective needs Corpus and Fau
Aibo children affective corpus are shown in Tables 2 and 3. It canbe seen that for the test set, the performance index of the attentiongate LSTM model is better than the traditional LSTM model.

Table 2 | Performance indicators of different LSTM models using emotional needs corpus of infantcrying database
Model

MeasureAngry

Hungry

Pain

Sad

Tired

AVG

Sample size
LSTM—
Recall
F1-score
Recall
F1-score
Recall
F1-score
Recall
F1-score

40
0.875
0.875
0.875
0.909
0.925
0.949
0.925
0.949

45
0.911
0.901
0.889
0.941
0.978
0.957
0.978
0.926

34
0.824
0.848
0.882
0.822
0.882
0.923
0.824
0.875

35
0.829
0.906
1.000
0.946
0.943
0.930
0.943
0.943

46
0.957
0.889
0.935
0.945
0.957
0.936
0.957
0.946

200
0.885
0.885
0.915
0.916
0.940
0.940
0.930
0.930

LSTM+deepf_1
LSTM+deepf_2
LSTM+deepf_3

Table 3 | Performance indicators of different LSTM models using Fau Aibo children’s emotion
corpus database
Model

Measure

A

E

N

P

R

AVG

Sample size
LSTM—
Recall
F1-score
Recall
F1-score
Recall
F1-score
Recall
F1-score

611
0.352
0.339
0.326
0.358
0.339
0.342
0.360
0.371

1508
0.373
0.373
0.300
0.338
0.289
0.340
0.327
0.363

5376
0.755
0.743
0.814
0.770
0.826
0.772
0.800
0.765

215
0.088
0.110
0.153
0.173
0.158
0.173
0.191
0.192

546
0.081
0.093
0.119
0.133
0.081
0.104
0.095
0.112

8256
0.594
0.586
0.621
0.603
0.625
0.601
0.619
0.604

LSTM+deepf_1
LSTM+deepf_2
LSTM+deepf_3

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5. CONCLUSION
In this paper, a child speech emotion recognition model based on
improved LSTM network is proposed frame-level speech features
are used instead of traditional speech features, the attention mechanism is introduced into the forgetting gate and the input gate of theinternal structure of the LSTM network model to form the attention gate. The experimental results show that the recognition rateof this model is significantly higher than that of traditional LSTM
model, and the recognition rate of depth model is higher than thatof shallow model. In CASIA database with other emotions, thetraining time of this model is shorter than that of LSTM model,
and the recognition rate is higher than that of LSTM model and
GRU model. The next step is to introduce this model into the fieldsof speech recognition, machine translation and lie detection, totest and study the continuous affective Corpus and to improve themodel for calculating attention scores, to further improve children’s
speech emotion recognition rate.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

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AUTHORS INTRODUCTION
Mr. Tianye Jian

Mr. Wanlong Peng

He has obtained a double bachelor degree inengineering and management in 2018. Now
he is a master student in Tianjin University of
Science and Technology. The major is control
engineering.

Dr. Yizhun Peng
He is an Associate Professor in the Schoolof Electronic Information and Automation,
Tianjin University of Science and Technology.
In 2002, he graduated from Nankai University
with a master’s degree in pattern recognition
and intelligent systems. He graduated from the
Chinese Academy of Sciences in 2006 with a
PhD in Control Theory and Control Engineering.

He is currently the master course student in
Tianjin University of Science & Technology.
Principle of automatic control, his research
field is about intelligent robot. During his
study, he has participated in and won awards asuniversity robot competition.

Mr. Zhou Yang
He is a graduate student majoring in Instrument
Science and Technology at Tianjin Universityof Science and Technology. His research direction is industrial robot.