Hubert-LSTM: A Hybrid Model for Artificial Intelligence and Human
Speech
ANTONIO-CRISTIAN BAIAS
The University Polithnica Of Bucharest, ROMANIA
Abstract: — Speech emotion recognition (SER) is a critical component of human-computer interaction, facilitating seamless
communication between individuals and machines. In this paper, we propose a hybrid model, integrating Hubert, a cutting-edge speech
recognition model, with LSTM (Long Short-Term Memory), known for its effectiveness in sequence modeling tasks, to enhance emotion
recognition accuracy in speech audio files. We explore the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
for our investigation, drawn by its complexity and open accessibility. Our hybrid model combines the semantic features extracted by
Hubert with LSTM’s ability to capture temporal relationships in audio sequences, thereby improving emotion recognition performance.
Through rigorous experimentation and evaluation on a subset of actors from the RAVDESS dataset, our model achieved promising results,
outperforming existing approaches, with a maximum accuracy of 89.1 %.
Key-words: — artificial intelligence, Hubert, LSTM, machine learning, Speech emotion recognition.
Received: March 21, 2024. Revised: September 3, 2024. Accepted: October 7, 2024. Published: November 7, 2024.
1. Introduction
Speech emotion recognition (SER) represents a pivotal
aspect of human-computer interaction, facilitating rapid and
natural communication between individuals and machines.
This field holds significant importance in various real-time
applications, enhancing human-machine interaction. Re-
searchers are actively engaged in the exploration of speech
signals captured through sensors for SER purposes,
constituting a dynamic domain within digital signal
processing. The primary objective is to discern the
emotional state of speakers by analyzing speech signals,
which inherently contain rich information beyond mere
verbal content [7-10]. Speech represents a complex and
richly nuanced form of communication, capable of
effectively conveying information [8]. Precise recognition of
emotions from speech audio files remains a challenging
issue [6] [11] [14] [16] [18]. This challenge is particularly
relevant in the context of my paper, where I explore the
integration of a hybrid model combining Hubert, a state-of-
the-art speech recognition model, with LSTM (Long Short-
Term Memory), a type of recurrent neural network known
for its effectiveness in sequence modeling tasks. By
combining the strengths of both models, we aim to enhance
the accuracy and robustness of emotion recognition in
speech audio files, addressing the complexities inherent in
understanding human emotions from speech signals.
2. Related work
This section reviews significant works in the field of self-
supervised speech learning, providing a foundation for the
development of the model presented in this study. The
wav2vec model was a crucial starting point in this direction,
using raw sound to learn useful representations for speech
recognition without needing the text labels of the sounds [2].
Advancing this idea, wav2vec 2.0 added a new component:
the model learns to differentiate between actual sound and
other random sounds, which helps the model to better
distinguish important features of speech [7]. Contrastive
Predictive Coding (CPC) applied a similar idea across
various fields, including audio, relying on the idea that
useful information can be obtained by comparing different
sections of sound to each other [1]. The HuBERT model
combines th-ese previous ideas, learning to predict certain
sound patterns in areas where information is hidden
(masked), which helps to gain a deeper understanding of the
structure and content of speech. Other studies have shown
that using deeper networks can improve the model’s ability
to understand the long and complex context of spoken
sounds [3] [20] [21] [24]. Additionally, vq-wav2vec brought
an improvement by introducing a layer that transforms
sounds into discrete symbols, making the model more
efficient and robust [6]. Therefore, these works have
contributed to the evolution of speech recognition
technologies, each adding new methods to extract
information from unlabeled audio data.
Recent advancements also include the application of
graph neural networks to speech emotion recognition, which
uses feature similarity and LSTM aggregators to enhance
model performance and interpretability [5] [12] [13] [15]
[17]. This development represents a significant step forward
in leveraging complex network architectures for speech
analysis, pointing to a broader trend of integrating diverse
neural network techniques to improve the depth and
accuracy of speech processing models [25-27].
3. Experiment details
For my investigation into emotional speech analysis, I
opted for the Ryerson Audio-Visual Database of Emotional
Speech and Song (RAVDESS). Two pivotal factors
influenced this decision. Firstly, RAVDESS is an open-
source dataset, promoting accessibility and fostering
collaboration within the scientific community. Its
availability fosters transparency and reproducibility in
emotional speech anal-ysis research. Secondly, the dataset’s
complexity presented an intriguing challenge. Researchers
have encountered difficulties achieving high performance on
RAVDESS, piquing my interest. The dataset’s diverse range
of emotional expressions and nuanced intensity levels offers
a unique opportunity to contribute to the refinement and
advancement of emotional analysis methods in speech.
The RAVDESS database is a validated multimodal
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repository designed for the analysis of emotional speech and
song. It features a gender-balanced cohort of 24 professional
actors delivering lexically-matched statements in a neutral
North American accent. The speech component covers
emotions such as calm, happiness, sadness, anger, fear,
surprise, and disgust. Each emotion is presented at two
intensity levels, supplemented by a neutral expression. This
comprehensive dataset serves as a solid foundation for in-
depth emotional analysis. In this research, the focus was
exclusively on the emotional speech component, aiming for
classification [4] [19] [20] [23].
The dataset comprises 1440 files, each uniquely identified
by a filename consisting of seven parts. The data loading
process utilizes the os.listdir() function to iterate through the
directory specified by the audiodir variable. Files with the
.wav extension are identified and added to the appropriate
lists of paths based on the dataset’s distribution. Labels are
assigned using predefined criteria extracted from the
filenames, which encode information about modality, vocal
channel, emotion, intensity, statement, repetition, and actor.
Exemple of audio file - (02-01-06-01-02-01-12.wav):
• Modality (01 = full-AV, 02 = video-only, 03 = audio-
only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad,
05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). There is
no strong intensity for the ’neutral’ emotion.
• Statement (01 = Kids are talking by the door, 02 = Dogs
are sitting by the door).
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd-numbered actors are male, even-
numbered actors are female).
Torchaudio library is employed to load and convert audio
files into tensor representations. The audio signals undergo
transformations, including conversion to mono and
resampling to ensure a standardized sampling rate of 16000
Hz. Feature extraction is performed using the Wav2Vec2
processor, facilitating effective analysis and interpretation
by the subsequent model. The Wav2Vec2 processor, a
robust creation by Facebook AI Research, serves as an
invaluable asset for feature extraction from audio signals
[2]. With its capabilities, it empowers the extraction of
crucial features from audio signals, meticulously preparing
the data for further analysis with machine learning models.
This processor can be easily imported and used in the
Python environment using the Hugging Face Transformers
library.
4. Proposed research
Speech emotion recognition (SER) is pivotal for voice
assistance and human-machine interfaces. This study
explores different approaches for emotion recognition using
the Hubert model, focusing on the RAVDESS dataset. Three
scenarios were analyzed, all employing the same pre-trained
Hubert model, with variations including data augmentation
and the integration of a hybrid Hubert-LSTM model.
Google Colab Pro was exclusively utilized for training,
leveraging its advanced resources, including a TPU
backend, 35 GB of system RAM, and a 225.8 GB disk
memory. The integration of a TPU backend from Google
Compute Engine significantly accelerated Python code
execution, particularly in machine learning tasks.
5. Classic Hubert with Wav2Vec2
processor (no augmentation)
In the quest to unveil the secrets of emotions expressed
in human vocal discourse, we embraced an advanced
approach - fine-tuning the HuBERT model, a cutting-edge
product developed by researchers in artificial
intelligence.This intricate process of tailoring the model to
the specifics of our dataset was meticulously orchestrated to
reveal the nuances of emotional expression in voice.
Hidden-Unit BERT (HuBERT) is an approach for self-
supervised speech representation learning, based on
techniques similar to those used in the BERT (Bidirectional
Encoder Representations from Transformers) model but
adapted for processing audio signals. Essentially, HuBERT
uses an offline clustering step to provide aligned target
labels for a prediction loss similar to that in BERT. A key
feature of HuBERT is applying the prediction loss only to
masked regions in the audio signal, forcing the model to
learn a combined representation of acoustic and linguistic
models for continuous speech inputs. This approach relies
primarily on the consistency of the unsupervised clustering
step rather than the quality of the cluster labels assigned.
HuBERT has demonstrated comparable or improved
performance compared to other models such as wav2vec 2.0
on various speech datasets, including Librispeech and Libri-
light. Using models with a large number of parameters,
HuBERT has achieved significant reductions in speech error
rates on challenging evaluation subsets [3]. The HuBERT
model was trained on vast unsupervised speech datasets like
LibriSpeech and Libri-light, which contain 960 and
approximately 60,000 hours of speech recordings,
respectively. These datasets offer diverse speech content,
including various accents and styles, ideal for training self-
supervised learning models. While HuBERT’s concepts can
be implemented in libraries like PyTorch or TensorFlow,
specific details about a ,,HuBERT model in Python” depend
on its implementation and training dataset.
HuBert with Wav2Vec2 model utilizes the pre-trained
Hubert architecture, and in the fine-tuning process
(additional training of the model on specific data), it
employs frozen weights of the pre-trained Hubert model.
This means that the layers of the neural network composing
the pre-trained Hubert model are not updated during
training on the RAVDESS dataset but are kept constant.
Instead, an additional classification layer is added,
initialized randomly, and trained to adapt to recognizing
emotions from audio speech. This additional classification
layer consists of a fully connected layer followed by a
dropout layer for regularization and then an output layer that
produces emotion predictions. During training, only the
parameters of this additional classification layer are
updated, while the parameters of the pre-trained Hubert
model remain unchanged. This method is efficient for
leveraging the pre-trained knowledge of the Hubert model
and adapting it to a specific task, such as recognizing
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emotions from speech audio, without needing to train the
entire model from scratch. Thus, we benefit from transfer
learning, which can lead to better results with less training
effort (see fig.1 HuBert with Wav2Vec2 processor).
Figure 1. HuBert model
6. Classic Hubert with Wav2Vec2
processor (augmentation)
The second model, data augmentation is applied by
adding Gaussian white noise to the audio signals. This type
of augmentation adds a small amount of random noise to the
original audio signals, thus simulating the natural variability
of the data. Gaussian white noise is a type of random noise
with a normal distribution, meaning that the levels of added
noise are drawn from a normal distribution with a certain
mean and standard deviation. This augmentation technique
can be useful in various machine learning tasks for audio
signal processing. Adding a controlled level of noise can
help increase the model’s generalization, making it more
robust to variability in the test data and reducing the risk of
overfitting on the training data. Additionally, it can help
enhance the model’s robustness to real-world noise in the
environment where it will be used.
7. HuBert-LSTM
By combining the pre-trained Hubert architecture with
recurrent neural networks (LSTM), we have created a hybrid
model that integrates the semantic features extracted by
Hubert with the LSTM’s ability to capture temporal
relationships in audio sequences. Within the LSTM model,
we used a single bidirectional LSTM layer with a hidden
size of 256 units. This bidirectional LSTM layer allows the
model to explore temporal information in both directions of
the audio sequence, thereby enhancing its ability to learn
complex temporal relationships. By jointly training these
two components, the model becomes capable of
understanding the temporal context of emotions and
extracting relevant semantic features, leading to improved
performance in the task of emotion recognition from audio
signals.
Figure 2. HuBert-LSTM
In the hybrid model, the Wav2Vec2 processor is utilized
to process the raw audio signals, extracting high-level
features. These features, along with the pre-trained weights
from the Hubert model, are then passed through additional
LSTM layers. These LSTM layers analyze the temporal
dynamics of the audio sequences, capturing long-term
dependencies and temporal patterns. By incorporating both
the semantic features extracted by Wav2Vec2 and the pre-
trained weights from Hubert into the LSTM layers, the
model can effectively learn and represent the complex
relationships between the audio features and the
corresponding emotion labels. This integration of different
components allows the hybrid model to leverage the
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strengths of each architecture, ultimately enhancing its
performance in emotion recognition tasks.
8. Results and discussion
To achieve precise accuracy on the RAVDES audio
data, we divided the 24 actors as follows: 20 actors for the
training set, 2 actors for the validation set, and 2 actors for
testing. Each folder representing an actor and contains a
total of 60 audio files. There are 8 files for each of the 7
classes of emotions and 4 audio files for the neutral class. In
total, there are 24 actors, resulting in a simple calculation of
1440 audio recordings (60x24).This is crucial because by
using new actors in the testing set, the model will
demonstrate how well it classifies new data that has not
been used in the training and validation processes before.
Furthermore, we trained the model 12 times, rotating the
actors each time to ensure that all actors go through the
training, validation, and testing processes, thus avoiding
subjectivity in the classification process. In the table 1, we
exemplify the results on the testing set obtained from
rotating the actors for each individual model. During the
evaluation on the test set, the Hubert model achieved a
maximum score of 0.833, while the Hubert model with data
augmentation (Hubert-aug) performed the best, with a score
of 0.841. Similarly, the model combining Hubert with
LSTM layers (Hubert-LSTM) achieved the highest score of
0.891. The best results, as mentioned, occurred with the pair
of actors Actor_07 and Actor_08, and on the opposite, the
poorest results were generated with the pair Actor_13 and
Actor14 (see line 6- of table 1).
TABLE I. RESULTS ON 12 FOLDS
ID
Hubert
Hub-
aug
Training Data
Validation
Data
Test Data
1
0.691
0.775
Actor 04: Actor
24
Actor 01,
Actor 02
Actor 03,
Actor 04
2
0.771
0.824
Actor 02+ Actor
06: Actor 24
Actor 03,
Actor 04
Actor 05,
Actor 06
3
0.833
0.841
Actor 04+ Actor
08: Actor 24
Actor 05,
Actor 06
Actor 07,
Actor 08
4
0.574
0.641
Actor 06+ Actor
10: Actor 24
Actor 07,
Actor 08
Actor 09,
Actor 10
5
0.775
0.683
Actor 08+ Actor
12: Actor 24
Actor 09,
Actor 10
Actor 11,
Actor 12
6
0.566
0.541
Actor 10+ Actor
14: Actor 24
Actor 11,
Actor 12
Actor 13,
Actor 14
7
0.591
0.625
Actor 12+ Actor
16: Actor 24
Actor 13,
Actor 14
Actor 15,
Actor 16
8
0.750
0.700
Actor 14+ Actor
18: Actor 24
Actor 15,
Actor 16
Actor 17,
Actor 18
9
0.708
0.700
Actor 16+ Actor
20: Actor 24
Actor 17,
Actor 18
Actor 19,
Actor 20
10
0.666
0.708
Actor 18+ Actor
22: Actor 24
Actor 19,
Actor 20
Actor 21,
Actor 22
11
0.716
Actor 20
Actor 21,
Actor 22
Actor 23,
Actor 24
12
0.705
Actor 02: Actor
22
Actor 23,
Actor 24
Actor 01,
Actor 02
13
0.705
average
These results suggest that adding LSTM layers significantly
improves the model’s performance, resulting in more
accurate predictions on the test dataset. There is
variation in the performance of the models across different
groups of actors, which may indicate the influence of
individual actor characteristics on model performance. In
the table 1, on line 13, the average of the results obtained by
rotating each pair of axes can be observed, ensuring that all
actors go through the testing, validation, and training
process. The HuBert model without augmentation achieved
an average of 0.696, while the augmented model obtained an
average of 0.705, approximately 1 percent better. The
Hybrid HuBert with LSTM model stands out significantly,
achieving a score of 0.735 on the RAVDES dataset,
approximately 3 percent more than the augmented model
and 4 percent more than the classic model without
augmentation. This highlights the importance of the hybrid
model in emotion classification, making it a powerful
model. Compared to article [1], which utilized a hybrid
LSTM-Transformer model on the same RAVDES dataset,
my hybrid model achieved better performance on the test
set. In that article, an average performance of 69.61% was
obtained, which is 4 percentage points lower than my hybrid
model. Additionally, in article [1], the cross-validation
method using 10 folds was employed, where 10% of each
emotion was kept for the test set. I find this approach
incorrect because new actors should be used for the test set
to observe how the model performs. Therefore, the
performance of 69.61%, it is considered less realistic. In the
figure 3, we depicted a graph highlighting the number of
correctly interpreted classifications (on the test dataset)
relative to each emotion class, representing the highest
accuracy of the Hubert-LSTM system. As observed in the
figure 3, the model performed best on the emotions angry
and fearful3, with all 16 correctly predicted recordings out
of 16. On the other end of the spectrum lies the neutral class,
where the model predicted 4 out of 8 audio files (50%). In
Figure 4, three graphs can be observed, highlighting the
performances of the HuBert-LSTM model on the training
dataset and validation dataset, wich represents acuracy and
the loss function.
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Figure 3. Predictions on best fold
Hubert-LSTM
Figure 4. Performance on best fold HuBert-LSTM
(graphic representation)
On the graph highlighting the performance on the
validation dataset , it reveals that the highest score is
achieved at epoch 6, representing an accuracy of 82.45%. In
table 2, we highlighted the performances on the test,
validation, and training datasets to illustrate their
differences across epochs. It is evident that the highest
accuracy was achieved on the test set, reaching 89.1%.
Conversely, on the training and validation sets, the best
performance was encountered at epoch 6, with an accuracy
of 80.5% on the training set and 82.4% on the validation set.
These findings suggest that the model demonstrates good
generalization ability, yielding better results on new, unseen
data (test set) com-pared to the data used for training and
validation. As indicated in Table 2, the columns for test
accuracy and test loss each contain a single value. This
occurs because the model was tested on data that were not
utilized during the training or validation phases.
Consequently, the test dataset was applied only after the
completion of six epochs.
The low values of the loss functions (table 2) across all
sets training, validation, and testing, are significant for
assessing the model’s performance. On the training set, the
loss function reached a minimum value of 0.580, indicating
the model’s ability to adapt well to the training data and
make precise predictions. Similarly, on the validation set,
the loss function obtained a value of 0.688, suggesting
adequate agreement between the model’s predictions and
the - the testing set, the loss function recorded the lowest
value of 0.459, indicating excellent generalization capability
of the model to make accurate predictions on unseen data.
These results are encouraging, suggesting that the model is
efficient and generalizable, demonstrating consistent
performance across various datasets. However, it is essential
to continue monitoring and evaluating the model’s
performance to identify and address any potential issues or
discrepancies.
TABLE II. PERFORMANCE ON BEST FOLD HUBERT-LSTM
(VALUE REPRESENTATION)
Epoch
Train
Accuracy
Validation
accuracy
Train
loss
VALIDATION
LOSS
Test
accuracy
Test
Loss
1
0.216
0.324
1.975
1.627
0.891
0.459
2
0.421
0.508
1.546
1.259
3
0.553
0.570
1.208
1.038
4
0.642
0.675
0.970
0.840
5
0.723
0.763
0.779
0.6881
6
0.805
0.824
0.580
0.689
According to the confusion matrix represented in figure
5 (best model HuBert-LSTM), to assess the effectiveness of
the emotion classification model, various standard metrics
such as precision, recall, and the F1 score were employed.
Precision, calculated as the ratio of true positives (TP) to the
sum of true positives and false positives (FP), measures the
accuracy of the model’s predictions for each emotion.
Mathematically, precision is represented as:
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(1)
Recall, also known as sensitivity, quantifies the model’s
ability to correctly identify positive instances from the total
actual positives by dividing TP by the sum of TP and false
negatives (FN). It is expressed as:
(2)
F1 Score = 2(PrecisonXRecall)/(Precison + Recall) (3) (3)
F1 score, the harmonic mean of precision and recall, offers a
balanced assessment of the model’s performance. It is
computed using the formula F1 overall accuracy of the
model, reflecting the ratio of total correct predictions to total
predictions made, provides a comprehensive measure of its
performance across all emotions. Therefore, the
combination of these metrics allows for a thorough
evaluation of both individual emotion classification and the
model’s overall efficacy. The evaluation metrics provide a
comprehensive overview of the emotion classification
model’s performance across various emotions. For the
,,Neutral” category, the model achieved a precision of 80%,
indicating that 80% of the predictions made for Neutral were
correct. However, the recall rate for Neutral was 50%,
suggesting that only half of the actual Neutral instances
were captured by the model. Consequently, the F1 Score,
which considers both precision and recall, settled at 61.54%,
representing a balanced measure of the model’s
effectiveness in classifying Neutral emotions. Moving to the
,,Calm” emotion, the model demonstrated a precision of
78.95% and a recall of 93.75%, resulting in an F1 Score of
85.71%, indicative of a strong performance in identifying
Calm emotions. Similarly, for the ,,Happy” category, the
model exhibited a precision of 71.43%, a recall of 93.75%,
and an F1 Score of 81.08%, reflecting satisfactory
performance in recognizing Happy emotions. The ,,Sad”
emotion showcased perfect precision at 100%, with a recall
rate of 75%, leading to an F1 Score of 85.71%, indicating a
robust overall performance for Sad emotions. In the case of
,,Angry” emotions, the model demonstrated high precision
(94.12%) and perfect recall (100%), resulting in an
outstanding F1 Score of 96.97%. Furthermore, for ,,Fearful”
and ,,Disgust” emotions, the model achieved perfect
precision, recall, and F1 Score, indicating exceptional
performance in identifying these emotions. Lastly, for
,,Surprised” emotions, the model exhibited a precision of
93.33%, a recall of 87.5%, and an F1 Score of 90.32%,
highlighting a strong capability in recognizing Surprised
emotions. The overall model performance, measured by
accuracy, was found to be 89.17%, underscoring the
model’s effectiveness in classifying emotions across the
dataset.
These metrics collectively provide valuable insights into
the model’s strengths and areas for potential refinement. In
addition to the evaluation of individual emotion categories,
it’s worth highlighting the robustness of the model’s
performance across various scenarios. The utilization of a
diverse dataset encompassing a wide range of emotions and
expressions ensures that the model is trained to generalize
well to real world scenarios. Moreover, the incorporation of
advanced architectures such as HuBert-LSTM contributes to
the model’s adaptability and ability to capture nuanced
patterns within the data. This adaptability is particularly
crucial in emotion recognition tasks where subtle variations
in facial expressions, tone of voice, and contextual cues play
a significant role. Furthermore, the high accuracy and
precision achieved across most emotion categories
underscore the model’s potential for practical applications
in fields such as affective computing, human-computer
interaction, and sentiment analysis. Overall, the
comprehensive evaluation metrics coupled with the model’s
adaptability and performance across diverse emotional
contexts demonstrate its efficacy and potential for real-
world deployment. Continued research and refinement in
this domain hold promise for further enhancing the model’s
capabilities and advancing the field of emotion recognition.
Figure.5. Confusion Matrix HuBert-LSTM on best fold
The graphical depiction in Figure 6, showcasing the
performance of the best model, Hubert-augmentation,
delineates the accuracy of predictions across different
emotion categories. Notably, in the ,,Neutral” category, the
model achieves a moderate performance, correctly
predicting 4 out of 8 examples, while misclassifying the
remaining 4. Conversely, the ,,Calm” emotion exhibits
outstanding performance, with 15 out of 16 examples
accurately predicted, showcasing the model’s robust
capability in this category. Similarly, the ,,Fearful” and
,,Disgust” emotions demonstrate high precision, with 15 out
of 16 examples correctly classified. On the other hand, the
,,Surprised” category presents a notable challenge for the
model, with only 11 out of 16 examples predicted correctly.
Overall, the graph suggests that the model excels
particularly well in classes such as ,,Calm,” ,,Fearful” and
,,Disgust” while facing difficulties in accurately classifying
instances of ,,Neutral” and ,,Surprised” emotions.
According to the graphs depicted in Figures 3 and 6, it
can be observed that the HuBert-LSTM model performed
significantly better on the ,,angry” emotion, correctly
classifying all 16 examples, compared to the HuBert-
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augmentation model, which classified 13 out of 16
examples correctly. Additionally, the HuBert-LSTM model
demonstrated superior performance on the happy” emotion
(15 out of 16 compared to 13 out of 16), ,,fearful” emotion
(16 out of 16 compared to 15 out of 16), and ”surprised”
emotion (14 out of 16 compared to 11 out of 16). The
HuBert-augmentation model generalized better only on the
,,sad” emotion (15 out of 16 compared to 12 out of 16).
Figure 6. Predictions on best fold Hubert-augmentation
In the analysis of the performance of the HuBert-
augmentation and HuBert-LSTM models, significant
differences in their graphical behavior are noticeable,
reflecting variations in accuracy and stability. Figure 7
illustrates a graphical representation of the performance of
the HuBert-augmentation model, while figure 4 depicts the
performance of the HuBert-LSTM model. A notable
observation is the more linear character of the graph
associated with the HuBert-LSTM model, as opposed to the
evident fluctuations in the case of the HuBert-augmentation
model, as analyzed in the table 3.
TABLE III. PERFORMANCE ON BEST FOLD HUBERT-AUGMENTATION (VALUE
REPRESENTATION)
Epoch
Train
Accuracy
Validation
accuracy
Train
loss
Validation
Loss
Test
accuracy
Test
Loss
1
0.274
0.385
1.843
1.378
0.841
0.625
2
0.502
0.622
1.291
1.150
3
0.621
0.719
1.010
0.687
4
0.777
0.763
0.678
0.619
5
0.793
0.859
0.578
0.499
Throughout the evaluation of the models, relevant
performance metrics were identified. Thus, the HuBert-
augmentation model achieved an accuracy of 79.3% on the
training set, 85.9% on the validation set, and 84.1% on the
test set. In contrast, the HuBert-LSTM model demonstrated
superior performance on the test set, recording an accuracy
of 89.1%. This indicates better adaptability of the HuBert-
LSTM model to the test data compared to the augmented
model. Additionally, a significant improvement in the loss
function on the test set of the HuBert-LSTM model is
noteworthy, which registered a value of 0.45 compared to
0.62 for the hybrid model. These differences underscore the
superior efficiency and robustness of the HuBert-LSTM
model compared to the augmented variant, suggesting better
adaptation to test data and enhanced generalization
capabilities.
-1
Figure 7. Performance on best fold HuBert-augmentation (graphic
representation)
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Figure 8. Confusion Matrix HuBert-augmentation on best fold
Upon analyzing the confusion matrix depicted in the
Figure 8, we derived the subsequent performance metrics:
In the ,,Neutral” category, precision reached 66.67% with a
sensitivity of 50.00%, resulting in an F1 score of 57.14%.
Conversely, for expressions denoting ,,Calm” and
,,Sadness,” results improved significantly, achieving
precisions of 57.69% and 93.75%, respectively. Sensitivities
for ,,Calm” and ,,Sadness” were also notable at 93.75%,
yielding F1 scores of 71.43% and 93.75%, respectively.
Noteworthy is the robust performance observed in
emotions such as ”Happy,” ”Fearful,” ”Dis-gust,” and
”Surprised,” with precisions ranging from 86.67% to
93.75%, sensitivities between 68.75% and 93.75%, and F1
scores between 78.57% and 93.75%.
A detailed analysis of the performance of the emotion
classification models, HuBert-LSTM and HuBert-
augmentation, reveals significant differences in their
effectiveness. Regarding the HuBert-LSTM model, it
demonstrates higher precision compared to HuBert-
augmentation for most emotions, such as Neutral (80% vs.
66.67%), Calm (78.95% vs. 57.69%), and Happy (71.43%
vs. 86.67%). The F1 scores of the HuBert-LSTM model are
also generally higher for these emotions compared to those
of the HuBert-augmentation model. However, the HuBert-
augmentation model achieves higher recall rates
(sensitivity) for certain emotions, such as Sad (93.75% vs.
75%), Fearful (93.75% vs. 100%), e and Disgust (93.75%
vs. 100%), compared to HuBert-LSTM. This suggests that
HuBert-augmentation may excel in capturing specific
emotions but at the cost of lower precision. Overall, despite
the higher performances of HuBert-augmentation in some
cases, HuBert-LSTM appears to provide a more precise and
balanced classification of emotions, thus highlighting the
advantages and limitations of each model in tackling this
complex task. Additionally, when considering emotions
such as Angry, Fearful, Disgust, and Surprised, both models
exhibit strong performances, with precision ranging from
86.67% to 93.75% and sensitivities between 68.75% and
100%. However, while HuBert-LSTM maintains perfect
precision for Sad emotions, achieving 100%, HuBert-
augmentation surpasses it in terms of sensitivity, with a
notable 93.75%. This discrepancy underscores the nuanced
differences in how each model handles specific emotional
nuances within the dataset. Moreover, the overall accuracy
of the models, a critical measure of their effectiveness in
classifying emotions across the entire dataset, reinforces
HuBert-LSTM’s superiority, standing at an impressive
89.17% compared to HuBert-augmentation. Despite these
differences, both models provide valuable insights into the
landscape of emotion classification, illuminating areas for
further refinement and optimization in future iterations.
Furthermore, delving deeper into the intricacies of model
performance reveals intriguing patterns. For instance, while
HuBert-LSTM showcases remarkable precision across a
spectrum of emotions, HuBert-augmentation excels in
capturing subtle variations in emotional expressions,
particularly evident in its higher sensitivity rates for certain
categories. This nuanced interplay between precision and
recall highlights the complex trade-offs inherent in emotion
classification tasks. Additionally, the disparity in
performance metrics underscores the need for a
comprehensive evaluation framework that considers not
only overall accuracy but also the model’s ability to discern
between nuanced emotional states. As advancements in
natural language processing continue to evolve, leveraging
these insights will be crucial in refining emotion
classification models to better serve diverse applications,
from sentiment analysis to affective computing. In the table
4, the performance of the HuBert-noaugmentation model on
the training, validation, and test sets is presented. During
training, there is a progressive improvement in accuracy,
with a significant increase from 27.25% in the first epoch to
84.5% in the fifth epoch. On the validation set, the model
continues to show improvements, reaching an accuracy of
85.96% in the same epoch. On the test set, the model also
achieves solid results, with an accuracy of 83.3%.
Regarding loss, there is a notable decrease, reaching a value
of 0.41 on the test set, indicating the model’s ability to adapt
well and make accurate predictions.
TABLE IV. PERFORMANCE ON BEST FOLD HUBERT-NOAUGMENTATION
(VALUE REPRESENTATION)
Epoch
Train
Accuracy
Validation
accuracy
Validation
Loss
Test
accuracy
Test
Loss
1
0.272
0.5
1.317
0.833
0.411
2
0.545
0.578
1.182
3
0.675
0.728
0.825
4
0.768
0.763
0.694
5
0.845
0.859
0.504
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DOI:10.37394/232025.2024.6.17
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These results underscore the reliability and
effectiveness of the HuBert-noaugmentation model in audio
data classification tasks.
In the figure 9, we graphically highlighted the
performances achieved on the training and validation sets
by the HuBert-noaugmentation model for each epoch. It is
worth mentioning that the model was trained up to the fifth
epoch, as beyond this point we encountered overfitting
issues.
Figure 9. Performance on best fold classic HuBert-noaugmentation
(graphic representation)
Analyzing the figure 10, it is noted that the graph
highlights a perfect performance of the prediction model for
the ,,happy” and ,,angry” categories, with all 16 predictions
being correct, without any errors in identifying these two
emotions.
This precision suggests that the model is excellently
calibrated to detect and differentiate be-tween facial
expressions or other features associated with these
emotional states. This indicates a profound understanding of
the distinctive characteristics of ,,happiness” and ”anger”
from the dataset used for training. Comparing fig. 3 with
fig. 10, it is evident that Hubert LSTM achieved a perfect
classification rate of 16 out of 16 instances for the ”fearful”
and ,,angry” classes, whereas the Hubert-noaugmentation
model achieved a similar flawless performance of 16 out of
16 instances for the ,,happy” and ,,angry” classes. This
demonstrates the robustness and efficacy of both models in
accurately categorizing emotional states, highlighting their
proficiency in handling diverse emotional expressions.
Fig.10. Predictions on best fold Hubert-noaugumentation
The detailed analysis of the confusion matrix results, as
depicted in the figure 11, highlights a diverse range of
performances in identifying different emotions. These data
were obtained from the confusion matrix of the classical
Hubert model without augmentation. Positive emotions such
as calmness and happiness exhibited precision and recall
values above 0.75, suggesting the model’s effectiveness in
detecting these positive states.
Particularly, happiness achieved the highest scores, with
precision, recall, and F1 score all at 0.941, indicating
excellent performance in identifying this emotion. On the
other hand, negative emotions like sadness, anger, and fear
generally achieved good scores but with some variations.
For instance, while sadness showed a precision of 0.65
and recall of 0.867, anger attained a precision of 0.889 and a
recall of 0.842. Conversely, fear attained a precision of
0.789, a recall of 0.938, and an F1 score of 0.857,
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suggesting the model’s high efficiency in identifying this
negative emotion. More complex or less frequent emotions
such as disgust and surprise demonstrated varied
performances.
For example, disgust achieved a perfect precision of 1.0
but a lower recall of 0.647, indicating high precision but
potential misses in some cases. Conversely, surprise
achieved a precision of 1.0, a recall of 0.917, and an F1
score of 0.957, highlighting the model’s efficiency in
identifying this emotion despite slightly lower recall. In
conclusion, the model exhibits overall good performance in
emotion recognition, with an emphasis on positive results
and variations in identifying negative or less frequent
emotions, suggesting the need for further adjustments to
improve performance.
+
Fig.11. Confusion Matrix HuBert-noaugmentation on best fold
A comprehensive comparison of HuBert-LSTM,
HuBert-augmentation, and HuBert-noaugmentation models
reveals distinct differences in their performance across
various metrics. HuBert-LSTM demonstrates superior
precision compared to both HuBert-augmentation and
HuBert-noaugmentation models across several emotions,
such as Neutral, Calm, and Happy. The precision rates for
HuBert-LSTM are consistently higher, suggesting its
effectiveness in accurately classifying these emotions.
How-ever, when it comes to recall rates, HuBert-
augmentation and HuBert-noaugmentation models exhibit
strengths in capturing specific emotions like Sad, Fearful,
and Disgust, achieving higher recall rates compared to
HuBert-LSTM in these cases. In terms of overall
performance, HuBert-LSTM appears to strike a balance
between precision and recall, providing a more robust
classification of emotions. On the other hand, while HuBert-
augmentation and HuBert-noaugmentation models may
excel in capturing certain emotions with higher recall rates,
they may sacrifice precision in the process. Ad-ditionally,
when evaluating emotions such as Angry, Fearful, Disgust,
and Surprised, all three models demonstrate strong
performances, with precision ranging from moderate to
high. However, there are nuanced differences in their
capabilities, with each model showing strengths and
weaknesses across different emotions.
While HuBert-LSTM, HuBert-augmentation, and
HuBert-noaugmentation models each offer unique
advantages, HuBert-LSTM stands out as providing a
balanced approach to emotion classification, with strong
precision across various emotions. Nonetheless, the choice
of model ultimately depends on specific requirements and
trade-offs between precision and recall in different emotion
recognition tasks.
9. Conclusion
The integration of HuBERT with LSTM into a hybrid
model for speech emotion recognition (SER) represents a
significant advancement in the field of human-computer
interaction. This hybrid model capitalizes on the strengths of
both architectures, combining HuBERT’s proficiency in
extracting se-mantic features from speech with LSTM’s
capability to understand and model the temporal dynamics
inherent in audio sequences. The utilization of the
RAVDESS dataset has underscored the model’s robustness
and versatility, enabling it to perform well across a wide
spectrum of emotional expressions and intensity levels. The
reported results indicate that the HuBert-LSTM model not
only surpasses the performance of traditional models but
also demonstrates considerable improvements over models
using either HuBERT or LSTM in isolation. With a
maximum accuracy of 89.1%, the hybrid model shows a
promising direction towards enhancing the accuracy and
efficiency of emotion recognition systems. This level of
performance is particularly noteworthy given the complexity
and variability of human emotional expression in speech,
highlighting the model’s ability to generalize across differ-
ent speakers and emotional states. Moreover, the hybrid
model’s superior performance in handling a diverse range of
emotions, including those that are typically challenging for
SER systems, such as neutrality and surprise, points to its
advanced capability in capturing the nuances of emotional
expression. The detailed analysis of precision, recall, and F1
scores across different emotions further confirms the
model’s efficacy, showcasing its balanced approach to
emotion classification. The success testing sets is a
testament to its generalization ability and robustness. These
qualities make it a highly effective tool for various real-
world applications, from enhancing user experience in
voice-assisted technologies to supporting mental health
assessments through emotional analysis.
In conclusion, the HuBert-LSTM hybrid model
represents a significant leap forward in the domain of speech
emotion recognition, offering a powerful, efficient, and
versatile tool for enhancing human-machine interactions. Its
successful application to the RAVDESS dataset highlights
its potential for broader implementation, promising to
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contribute significantly to advancements in affective
computing and beyond. Continued research and refinement
of this model could further unlock its capabilities, paving
the way for more intuitive and empathetic computer
systems. For those interested in my research, I have added a
footnote containing a link
2
to the code for all three models,
as well as a code testing the Hubert-LSTM model on
unlabeled data.
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Policy)
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stages from the formulation of the problem to the
final findings and solution.
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Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The author has no conflict of interest to declare that
is relevant to the content of this article.
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DOI:10.37394/232025.2024.6.17
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E-ISSN: 2692-5079
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Volume 6, 2024