Alex-Răzvan Ispas
email: alex-razvan.ispas@universite-paris-saclay.fr

Théo Deschamps-Berger
email: theo.deschamps-berger@lisn.upsaclay.fr

A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions

Keywords:

Figure 1: Overview of the proposed multi-task, cross-attention speech emotion recognition architecture. The acoustic and linguistic features are extracted with the help of HuBERT [13] and DeBERTaV3 [12]. Afterwards the features are refined by two self-attention modules. Then, inspired by [8] and [21]

, we slightly modify the original modality interaction and introduce two multihead cross-attention layers which integrate the learnable bridge tokens that create the connection between the features of the two modalities. The model predicts the categorical emotion label, the valence, and the arousal score for each segment. Besides the cosine decaying procedure, the random modality masking is performed according to the original implementation.

INTRODUCTION

The capacity to automatically estimate human users' emotions during conversations is crucial for adaptive intelligent technology. Speech is one of the most important means of communication among human beings. The paralinguistic information conveyed through speech is used to identify people's emotional states. There are two major paradigms of emotion which are referenced in the current work for affective computing. The first is the categorical representation paradigm promoted by figures like Plutchick or Eckman [START_REF] Ekman | An argument for basic emotions[END_REF] who classify the emotions into groups of primary emotions (e.g surprise, anger, happiness, sadness, fear and disgust). The second is a continuous dimension paradigm proposed by Russel [START_REF] James | Evidence for a three-factor theory of emotions[END_REF] which tries to quantify emotions based on three continuous dimensions: valence, arousal and dominance.

The IEMOCAP [START_REF] Busso | IEMOCAP: Interactive emotional dyadic motion capture database[END_REF] dataset presents a compelling multi-modal approach to emotion analysis, combining audio and video recordings of naturalistic dyadic conversations. With diverse emotion labels representing categorical and dimensional paradigms, it offers a rich landscape for exploring and understanding human emotional expressions. Speech corpora like IEMOCAP are valuable resources which open possibilities for exploring the complex relationship between behavioral signals and emotional understanding in natural language processing and affective computing.

While there have been successful papers which provided remarkable results for categorical emotions [START_REF] Aftab | LIGHT-SERNET: A lightweight fully convolutional neural network for speech emotion recognition[END_REF][START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF][START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF][START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF], as well as for dimensional emotions [START_REF] Shamsi | Training speech emotion classifier without categorical annotations[END_REF][START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF], there are few works that have tried to use both paradigms at the same time. For example, [START_REF] Shamsi | Training speech emotion classifier without categorical annotations[END_REF] tried to predict the categorical emotions by mapping the dimensional dimensions, meanwhile in a recent work [START_REF] Sharma | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition[END_REF], the categorical and dimensional emotions are unified through a hierarchical multi-task model obtaining notable results for the arousal dimension. However, there are examples where multi-task learning proved to slightly improve the performance of other speech tasks [START_REF] Feng | End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations[END_REF].

Multi-task learning holds promise for speech emotion recognition due to its capacity to leverage shared information between related tasks. By simultaneously optimising the model for both categorical emotion labels and continuous dimensional representations, multi-task learning enables the extraction of a more holistic and nuanced understanding of emotions. Therefore, in this paper, our aim is to demonstrate the performance contribution of multi-task learning by predicting the categorical and dimensional emotions through both acoustic and linguistic information. Considering the recent success of cross-attention between the features of the self-supervised pre-trained models of the textual and audio representations [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF][START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF], we utilise a similar approach and obtain better performance on dimensional representations. We propose a multi-task architecture that predicts the categorical and the dimensional emotions at the same time, but independently. As we want to give equal importance to the acoustic and linguistic features, we adopt a similar strategy emphasised by [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF] which introduces a set of learnable query tokens which are meant to collect the emotional information from the concatenated features of the audio and textual modality. Unlike the original approach, the tokens will play the role of a bridge that will fuse the acoustic and linguistic features together. At the same time, we experiment with the Random Modality Masking (RMM) which masks either the audio or the text embeddings during the training in order to constrain the model to learn in depth the features of each modality.

Our experiments demonstrate the efficiency of cross-regularisation through categorical and dimensional multi-task learning by comparing our results with the experiments where we optimize for a single task. In addition, our best performing model achieves remarkable results for the valence dimension.

RELATED WORK

In the case of IEMOCAP [START_REF] Busso | IEMOCAP: Interactive emotional dyadic motion capture database[END_REF], there has been a lack of agreement regarding the number of folds as well as the number of emotional segments, making the reproducibility of the results a challenging task. Therefore, [START_REF] Antoniou | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP[END_REF] made a review of the current IEMOCAP papers, that provided open-source implementations, in order to check their reproducibility. They also provided a set of recommendations and evaluation guidelines in which they state that a minimally comparable baseline should use the neutral, sad, angry and happy+excited classes, resulting into a total number of 5531 samples. They also recommend the use of 10-fold cross-validation where 8 speakers are used for training, 1 speaker for validation and 1 for test. Once these results are reported, the researches can modify the experiments as desired.

In the context of categorical emotions, the current state-of-theart is dominated by transformer based architectures. In the case of IEMOCAP [START_REF] Busso | IEMOCAP: Interactive emotional dyadic motion capture database[END_REF], fine-tuning approaches on HuBERT-large [START_REF] Hsu | Hubert: Self-supervised speech representation learning by masked prediction of hidden units[END_REF] and wav2vec2-large [START_REF] Baevski | wav2vec 2.0: A framework for self-supervised learning of speech representations[END_REF] prove to be the most efficient uni-modal methods for categorical emotions [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF]. Other approaches try to use complementary tasks, such as speech recognition in order to crossregularize the models [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF]. In multi-modal systems, cross-attention mechanisms can provide an increase of performance of 4% [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF] for other categorical emotion corpora such as CEMO, an emercency call center corpus [START_REF] Deschamps-Berger | End-toend speech emotion recognition: challenges of real-life emergency call centers data recordings[END_REF]. In [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF], they introduce random modality masking to mitigate the issue of bias modality by maximazing the emotional information provided by both the audio and text transformer. They also introduce a group of learnable tokens in the cross-attention layer such that they can fuse the emotional features which come from the acoustic and linguistic modalities.

In the case of dimensional emotions, pre-trained transformerbased architectures show better performance for valence on speech corpora, making them more robust for small perturbations and group fairness for the two genders [START_REF] Wagner | Dawn of the transformer era in speech emotion recognition: closing the valence gap[END_REF]. [START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF] proposed a Teacher-Student training approach on multi-modal audio and text representations in which they achieved state-of-the-art results for dimensional values.

Although there have been successful experiments that used speech-to-text in a multi-task set up to enhance the performance of categorical emotions [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF][START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF], there are few papers which considered to combine the categorical and dimensional emotions. In a recent paper, both the categorical and dimensional emotions were used in a hierarchical multi-task approach [START_REF] Sharma | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition[END_REF]. Although the result for valence was higher, it had a slightly lower score for arousal compared to [START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF]. [START_REF] Chen | Multimodal multitask learning for dimensional and continuous emotion recognition[END_REF] explored the prediction of multiple emotions by using multi-task learning together with every available modality (acoustic, visual and textual) in a temporal LSTM-RNN model. In another study, the arousal and valence were leveraged to assign supplementary information within a Deep Belief Network framework for the categorical task [START_REF] Xia | A multi-task learning framework for emotion recognition using 2D continuous space[END_REF].

In the context of our current work, we are going to respect the guidelines provided by [START_REF] Antoniou | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP[END_REF] and compare against the other models that use the same setup. At the same time, we will follow [START_REF] Wagner | Dawn of the transformer era in speech emotion recognition: closing the valence gap[END_REF] and use the weighted average recall (WAR) as the categorical metric since IEMOCAP is an imbalanced dataset. In the case of dimensional emotions, the metric will be the Concordance Correlation Coefficient (CCC). Our work tries to explore the possibility of performance enhancement in SER multi-modal frameworks by using both the categorical and dimensional emotions in a multi-task manner. Regarding the transformers, this work will use HuBERT-large and DeBERTaV3-large [START_REF] He | Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing[END_REF]. We experimented with different combinations of the dual cross-attention emphasised in [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF], the random modality masking (RMM) and the learnable query tokens from [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF]. Our best performing model achieves a CCC score of .748 for valence.

METHODOLOGY

Given the audio segment and the textual transcript of someone's speech, our model is predicting the categorical emotion label, the valence and the arousal. The two transformers used to extract the acoustic and linguistic features are HuBERT-large [START_REF] Hsu | Hubert: Self-supervised speech representation learning by masked prediction of hidden units[END_REF] and DeBERTaV3-large [START_REF] He | Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing[END_REF]. HuBERT-large training implies self-supervised learning in which the targets are created based on predicting clusters in which each feature vector is assigned, meanwhile DeBERTaV3large relies on disentangled attention in order to capture dependencies between the words in the sentence. Figure 1 presents the overview of the proposed architecture. Section 3.1 introduces the multi-task loss as well as its sub-components. In Section 3.2 we present the feature refinement as well as how we handled the sequence length issue between the audio and text embeddings. Then Section 3.3 presents how the emotional features of the acoustic and linguistic embeddings are learned by implementing a set of learnable bridge tokens. Section 3.4 presents the way the complementary features are fused with the help of multi-head attention and the learnable bridge tokens. Finally, Section 3.5 presents the Random Modality Masking (RMM) and the masking chance decaying procedure chosen alongside the training.

Multi-task Learning

Our multi-task problem can be formalised as a prediction of a set consisting of valence, arousal, and the categorical emotions which can take values from the set D = {𝑛𝑒𝑢𝑡𝑟𝑎𝑙, ℎ𝑎𝑝𝑝𝑦, 𝑠𝑎𝑑, 𝑎𝑛𝑔𝑟𝑦}. Given a set of features {𝑢 𝑖 , 𝑡 𝑖 } 𝐼 𝑖=1 , where 𝑖 ∈ {1, . . . , 𝐼 } represents the i-th utterance of the speech 𝑢 𝑖 and the i-th transcript 𝑡 𝑖 , predict the target

𝑦 𝑖 = [𝑦 𝑖 𝐷𝑖𝑠𝑐 , 𝑦 𝑖 𝑉 , 𝑦 𝑖 𝐴 ],
where 𝑦 𝑖 represents the ground truth.

The categorical labels will be optimised using the categorical cross-entropy (CE), which for a given dataset 𝐼 is computed as:

𝐶𝐸 = 𝐼 ∑ 𝑖=1 𝐷 ∑ 𝑐=1 𝑦 𝑖,𝑑 log( ŷ𝑖,𝑑 )
In the case of dimensional emotions, the standard loss function is the inverse of the Concordance Correlation Coefficient (CCC). That is because the CCC score needs to be maximised, meanwhile the loss needs to be minimised. Given the vector of predictions ŷ ∈ R 𝐼 , and the ground truth 𝑦 ∈ R 𝐼 , the CCC loss for one dimensional value can be calculated as:

L 𝐶𝐶𝐶 = 1 - 2𝐶𝑜𝑣 ( ŷ, 𝑦) 𝜎 2 ŷ + 𝜎 2 𝑦 + (𝜇 ŷ -𝜇 𝑦 )
Following the example of [START_REF] Feng | End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations[END_REF][START_REF] Sharma | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition[END_REF], we define our multi-task loss as the weighted sum of the categorical cross-entropy, the CCC loss for valence and the CCC loss for arousal, as shown in Equation 3:

L 𝑡𝑜𝑡𝑎𝑙 = ℎ 1 * 𝐶𝐸 + ℎ 2 * L 𝑉 𝐶𝐶𝐶 + ℎ 3 * L 𝐴 𝐶𝐶𝐶 3 𝑖=1 ℎ 𝑖 = 1
where ℎ 1 , ℎ 2 , ℎ 3 represent the hyperparameters that control the contribution of each criterion in the learning process.

Uni-modal Refinement

Before performing cross-attention, we refine the encoded features with the help of the self-attention modules in order to emphasise the emotional frames for each modality. In our architecture, each self-attention module consists of 32 attention heads, which is the same amount of heads utilised for the large models in [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF]. Both HuBERT-large and DeBERTaV3-large have the same hidden dimension of 1024. In order to have the same representation regarding the sequence length, the shorter sequence is padded to the dimension of the longer one. The final representation of the audio and text features are 𝑎 1:𝑁 ∈ R 𝑁 𝑥𝑑 𝑒 and 𝑡 1:𝑁 ∈ R 𝑁 𝑥𝑑 𝑒 where 𝑁 is the maximum sequence length and 𝑑 𝑒 is the hidden dimension size.

Modality Interaction

Following the example of [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF], we create two groups of trainable tokens which aggregate the embedding features of 𝑎 ′ 1:𝑁 and 𝑤 ′ 1:𝑁 by the attention mechanisms. Unlike the classic cross-attention, where the key and the value come from the same modality and the query comes from a different modality, our approach uses different modalities for the key and value and it tries to merge them together by implying a set of learnable bridge tokens. The trainable tokens 𝑄 𝑎 ∈ R 𝐿𝑥𝑑 𝑒 and 𝑄 𝑡 ∈ R 𝐿𝑥𝑑 𝑒 play the role of the query. Tensor 𝑄 𝑎 represents the learnable query tokens (Q) of the multihead attention layer that uses the audio embeddings as key (K) and the text embeddings as value (V), meanwhile 𝑄 𝑡 is the query for the layer where the text embeddings are the key and the audio embeddings are the value. The learnable bridge tokens can be perceived as the intermediary that provides a mean of interaction between the acoustic and linguistic features. Dimenson 𝐿 represents the number of learnable tokens. We perform cross-attention in both scenarios because it is possible that the attention weights computed with the help of either the audio or the text embeddings can offer more or less insight regarding the optimal weights for fusing the features together.

Once the cross-attention is performed, each tensor will contain L tokens that are average pooled over the tokens dimension. Afterwards, the two vectors will be stacked and average pooled once again in order to get the mean emotional features from the two multi-heads. Finally, the mean vector will be fed into both the classifier and the regressor in order to predict the categorical emotion label,the valence and the arousal. The architecture of the classifier and the regressor can be seen in Figure 2.

Fusing the results of the multi-head cross-attention layers

As explained by [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF], the main intuition behind attention is to weight each matrix input with respect to its importance given by the context. Cross-attention implies combining embeddings of the same dimension which come from different modalities. In the context of our architecture, instead of using a query (Q) and a key (K) from two different modalities, the queries will be learnable tensors and the key and value will come from the two modalities.

𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑆 = (𝑄, 𝐾, 𝑉 )) = 𝑠𝑜 𝑓 𝑡𝑚𝑎𝑥 (

𝑄𝐾 𝑇 √ 𝑑 𝑘 )𝑉
where 𝑆 ∈ {(𝑄 𝑎 , 𝐾 𝑎 , 𝑉 𝑡 ), (𝑄 𝑡 , 𝐾 𝑡 , 𝑉 𝑎 )}

In the case when we perform cross-attention without the learnable bridge tokens, we follow the norm and we choose the key and the value to come from one modality and the query from the opposite modality.

As shown in Figure 1, we followed [START_REF] Deschamps-Berger | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus[END_REF] and [START_REF] Vaswani | Attention is all you need[END_REF] and we implemented two multi-head attention layers such that we could apply attention mechanisms on multiple subspaces. 𝑀𝑢𝑙𝑡𝑖ℎ𝑒𝑎𝑑 (𝑄, 𝐾, 𝑉 ) = 𝐶𝑜𝑛𝑐𝑎𝑡 (ℎ 1 , . . . , ℎ 𝑛 )𝑊 𝑂 where ℎ 𝑖 = 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄𝑊 𝑄 𝑖 , 𝐾𝑊 𝐾 𝑖 , 𝑉𝑊 𝑉 𝑖 ) Once the emotional features 𝑒 𝑎 and 𝑒 𝑡 are obtained from the multi-head cross-attention layers, the stacked mean feature embeddings 𝑒 𝑠 are computed as following: 𝑒 𝑠 = 𝑀𝑒𝑎𝑛(𝑆𝑡𝑎𝑐𝑘 (𝑀𝑒𝑎𝑛(𝑒 𝑎 ), 𝑀𝑒𝑎𝑛(𝑒 𝑡 )))

Random Modality Masking

As stated in [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF], the main purpose of random modality masking (RMM) is to mitigate the situations where only the features of a single transformer are used to detect the emotions. The strategy of RMM is to encourage the model to focus on the uni-modal features, in the early stages, by randomly masking either the text or the audio embeddings. In the later stages, the model will focus more on the complementary features of the multi-modal system. In order to not alter the weights of the masked modality, when the model applies RMM we freeze the layers of the respective modality.

Like in the original work, we set a hyperparameter p which dictates if the respective epoch has one modality masked or not. The training starts with p set to 0.8 and it decays in a cosine manner alongside the training, as shown in Figure 3. If the probability goes below the threshold of 0.1, it is set automatically to 0. The original paper emphasised that the model is inclined to detect the emotions by relying on the features of the text transformer. Therefore, we set the RMM to be more prone to mask the textual modality. The probability of masking the text embeddings is 0.6, meanwhile the one for audio is 0.4. The IEMOCAP dataset contains 12 hours of recorded audiovisual data, featuring five spontaneous conversational sessions between a unique male and female speaker. There are 10 speakers in total alongside 5 different sessions. Three annotators labeled each segment with categorical emotions (e.g., happy, angry, sad etc.) and provided dimensional values for the valence, arousal, and dominance dimensions. The conversations are divided into scripted and improvised sections.

Experimental Set Up

The experiments were performed based on the guidelines emphasised by [START_REF] Antoniou | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP[END_REF]: we used only the neutral, sad, angry and happy + surprised labels, resulting into a total of 5531 utterances. The number of utterances for each emotion are: 1708 for neutral, 1139 for angry, 1084 for sad and 1646 for happy. In the case of dimensional emotions, we rescaled the scores from 0 to 5, into 0 to 1. At the same time, all experiments were conducted using the 10-fold validation.

Our experimental results are displayed in Table 2. While training, we use the multi-task loss presented in Section 3.1 with all hyperparameters set to 0.33 for an equal contribution. During the initial phases of our experimentation, adjustments to hyperparameters were explored, such as assigning of 0.5 to categorical emotions and 0.25 to each dimensional variable. However, this led to a slight reduction in the performance of all tasks. Consequently, we decided to maintain the balanced weight configuration. We used the Adam [START_REF] Diederik | Adam: A method for stochastic optimization[END_REF] optimiser, with a learning rate of 3 -5 and a batch size of 16. To prevent gradient explosion in the two larger models, we implemented gradient norm clipping, setting the threshold to 1.

In the experiments with learnable bridge tokens, we set the number of tokens to 30 as it was the optimal number of tokens reported in [START_REF] Wang | Exploring Complementary Features in Multi-Modal Speech Emotion Recognition[END_REF]. Each multi-head attention layer had a total of 32 heads. The embedding layers, as well as the feature extractor layers, were frozen in both modalities. Each fold was trained for 20 epochs. [START_REF] Zou | Speech emotion recognition with co-attention based multi-level acoustic information[END_REF] MFCCs, Spec, wav2vec2 71.64% Wang et.al [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF] HuBERT-large 79.6% (73.0) Wang et.al [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF] wav2vec2-large 77.5% (71.0) Cai et. al [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF] wav2vec2-large ✓ 78.15%

RESULTS

Comparing Results

We compare our results against the other reported experiments that used 10-fold. The previous categorical results can be seen in Table 1. We gathered the same results reported by [START_REF] Wagner | Dawn of the transformer era in speech emotion recognition: closing the valence gap[END_REF] and [START_REF] Antoniou | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP[END_REF]. All experiments that were conducted with multi-task learning utilised speech-to-text as a secondary task. We chose the WAR metric as the primary one for categorical emotions because the dataset is imbalanced as shown in Section 4.2. Therefore, we want to take into account the class frequencies and assign higher importance to the recall of the minority classes. Like that, we prevent the model from being biased towards the majority label and ensure that the performance on all four labels are considered.

In Table 1, the last results are obtained by using multi-task. The multi-task loss of [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF] implies the categorical cross-entropy (CE) and the Connectionist Temporal Classification (CTC) loss. In the case of the best performing model [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF], they perform partial fine-tuning on a HuBERT-large model whose output is average time pooled and fed into a linear classifier. The average time pooling is compressing the variance of the time lengths by aggregating the hidden states of the model over time.

To the best of our knowledge and considering the fact that the guidelines suggested by [START_REF] Antoniou | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP[END_REF] are recent, we could not find dimensional experiments that used 10-fold on IEMOCAP. Therefore, Table 3 contains the best performing results for dimensional emotions on 5-fold. [START_REF] Sharma | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition[END_REF] combined the CE together with the CCC loss of the valence, arousal and dominance.

Our Results

Table 2 displays the results of our experiments. We chose to also display the Unweighted Average Recall (UAR) in order to emphasise the difference in performance when we do not take the sample frequency into account. If we compare the single-task and multitask experiments from Table 2, every experiment that uses both the categorical and dimensional emotions improves the CCC score of the valence. In this configuration, this result suggests that the prediction of the categorical emotions can cross-regularize the prediction of valence. This statement is also supported by the fact that the model underperforms when it is trained only for the dimensional task. Overall, in the best performing models, the multi-task improves the score of valence by 2-3%. In the case of arousal, it was noticed that in the early epochs it was performing much better than the valence. However, in the middle stages of the training, the arousal is surpassed by the valence in almost every experiment. This behaviour could be caused by the fact that the weights of the regressor cannot be used to predict both values at the same time. The performance of the categorical emotions also benefits from multi-task learning, increasing the performance of the WAR by 1-2% compared to the single task configuration. However, the best performing model for WAR (74.6%) has a slightly lower performance compared to the other categorical experiments such as [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF] and [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF]. This result could imply that the CTC loss used by [START_REF] Cai | Speech emotion recognition with multi-task learning[END_REF] is a more optimal complementary task in contrast with the regression of the dimensional emotions. In the case of [START_REF] Wang | A finetuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding[END_REF], unlike the standard average pooling, the average time pooling layer is considering the entire temporal dimension and it computes the average across all time steps, which seems like a more consistent method for inferring the information of the acoustic features. Overall, the multi-task learning increased the performance of every experiment, no matter the complexity of the architecture.

By looking to the components of the architecture, it could be noticed that the self-attention layers play a major role in the performance, in spite of the number of optimised tasks. Our multi-task model which uses only self-attention increases the WAR of our HuBERT-large + DeBERTaV3-large baseline by almost 6% and the CCC score of the valence by 10%. The improvement is caused by the effectiveness of the emotional frames emphasised from each modality. Together with cross-attention, the model can reach a valence score of .738. When the learnable bridge tokens are included, the model reaches the highest score for the CCC of the valence (.748) and the second best score for the WAR (74.6%), which suggests that [START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF] wav2vec2-large .472 .735 Srinivasan et al. [START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF] HuBERT-large .485 .733 Srinivasan et al. [START_REF] Srinivasan | Representation learning through cross-modal conditional teacher-student training for speech emotion recognition[END_REF] HuBERT-large .547 .752 Roshan et al. [START_REF] Sharma | Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition[END_REF] ✓ .660 .717 the learnable bridge tokens can offer insight regarding the way of how the acoustic and linguistic features can be fused together.

Unfortunately, the RMM could not improve the average performance of our best model. However, the variance in performance is slightly lower across the three seeds. The underperformance of the RMM approach could be the lack of uni-modal training during the first epochs. When the RMM is applied, only the transformer whose modality features are masked gets its weights frozen. However, both multi-head attention layers are trained together. The features learned while training the two multi-head attention layers might not be beneficial for both of them. Therefore, the model could benefit more from the RMM if the multi-head attention layers are trained separately in the early stages. The classifier and regressor are also trained at the same time during all epochs. A fine-tuning epoch for both the classifier and the regressor could slightly improve the results at the end of the training. Last but not least, it has been noticed that the arousal performance downgrades in favour of the valence, which makes us consider the implementation of different regressors for each dimensional value.

CONCLUSION

To conclude, we proposed a multi-task multi-modal system for predicting the categorical and dimensional emotions in spontaneous conversations. In our configuration, we compare our multi-task results with the single-task ones and emphasize that the crossregularisation of the two tasks is beneficial for the performance of the categorical emotions and for the valence. In our setup, our approach achieves the best results by applying two groups of learnable bridge tokens that fuse the features of the two modalities through the help of the multi-head attention layers. Our best performing multi-task, multi-modal framework achieves a CCC score of .748 for valence.

Unlike the previous approaches, the performance of the valence surpasses that of the arousal when the model complexity is increased. For future work, we plan to investigate the reason why the pattern gets inverted. Additionally, we intend to increase our performance by implementing the suggested improvements mentioned in the results section. We will also dive deeper into the learnable bridge tokens and find the optimal number of tokens for our configuration.

REPRODUCIBILITY

The experiments were performed in PyTorch on two types of GPU: RTX A6000 and Tesla V100. The training of a 10-fold lasted between 16 and 24 hours depending on the number of epochs and the chosen GPU. In order to ensure reproducibility, all experiments were conducted on the same seed and were prevented from using non-deterministic algorithms. The multi-task experiments were validated by calculating the average of three distinct seeds. In order to reproduce our experiments, use the seeds 1,2 and 3 for both the CUDA and CPU versions of PyTorch. At the same time, set the PyTorch CuDNN to deterministic.

Figure 2 :

 2 Figure 2: The head consists of a classifier and a regressor. The regressor has two sigmoid neurons which predict the valence and the arousal.

Figure 3 :

 3 Figure 3: Masking chance cosine decay alongside the training of a fold. When the masking chance gets below 0.1, it is set by default to 0

Table 1 :

 1 Comparing Results for categorical Emotions. All experiments were conducted on 10-fold. M-T means that the respective experiment used multi-task, WAR stands for Weighted Average Recall

	Publication	Acoustic Features	M-T WAR
	Feng et al. [10]	MFCCs	68.63%
	Aftab et al. [1]	MFCCs	70.23%
	Zou et al.

Table 2 :

 2 Experimental results on the proposed architecture. The experiments where multi-task learning was performed have both the Disc and Con columns check-marked. The table specifies the components that were used in each experiment. S/A signifies that the model used self-attention, meanwhile CR/A means that the model used cross-attention layers. Q specifies if the architecture learned bridge tokens and the RMM marks that the model is applying random modality masking during training. The metrics for categorical emotions are the Unweighted Average Recall (UAR) and the Weighted Average Recall (WAR). V and A represent the Concordance Correlation Coefficient (CCC) of the Valence and Arousal. Every experiment which implied multi-task learning was conducted on three different seeds and it displays the mean and the standard deviation.

	Model	S/A CR/A Q RMM Disc Con	UAR	WAR	V	A
	HuBERT-large + RoBERTa-large				✓	✓	68.3 ± 0.20% 66.31 ± 0.34% .638 ± .01 .673 ± .03
	HuBERT-large + RoBERTa-large				✓		66.18%	65%	-	-
	HuBERT-large + RoBERTa-large					✓	-	-	.620	.674
	HuBERT-large + DeBERTav3-large				✓	✓	69.43 ± 0.64% 67.22 ± 0.74% .637 ± .43 .674 ± .12
	HuBERT-large + DeBERTav3-large				✓		67.12%	65.5%	-	-
	HuBERT-large + DeBERTav3-large					✓	-	-	.617	.675
	HuBERT-large + DeBERTav3-large ✓				✓	✓	73.92 ± 0.17% 73.01 ± 0.25% .737 ± .04 .676 ± .07
	HuBERT-large + DeBERTav3-large ✓				✓		74.17%	72.9%	-	-
	HuBERT-large + DeBERTav3-large ✓					✓	-	-	.711	.687
	HuBERT-large + DeBERTav3-large ✓	✓			✓	✓	74.68 ± 0.16% 74.69 ± 0.18% .738 ± .03 .685 ± .03
	HuBERT-large + DeBERTav3-large ✓	✓			✓		74.29%	73.6%	-	-
	HuBERT-large + DeBERTav3-large ✓	✓				✓	-	-	.718	.686
	HuBERT-large + DeBERTav3-large ✓	✓	✓		✓	✓	75.71 ± 0.17% 74.6 ± 0.21% .748 ± .05 .677 ± .04
	HuBERT-large + DeBERTav3-large ✓	✓	✓		✓		74.02%	73.05%	-	-
	HuBERT-large + DeBERTav3-large ✓	✓	✓			✓	-	-	.714	.698
	HuBERT-large + DeBERTav3-large ✓	✓	✓	✓	✓	✓	75.79 ± 0.37% 74.38 ± 0.33% .744 ± .02 .679 ± .04
	Table 3: Comparing Results for dimensional Emotions. All
	experiments were conducted on 5-fold. M-T means that
	the respective experiment used multi-task. WAR stands for
	Weighted Average Recall
	Publication	Acoustic Features M-T	V	A
	Srinivasan et al. [18]	wav2vec2-base		.363 .728
	Srinivasan et al.

ACKNOWLEDGMENTS

We would like to thank Emmet Strickland for their assistance in proofreading this paper. His careful review and feedback were invaluable in improving the clarity and correctness of our work.


Marion Blondel

Christelle Dodane

Karine Martel

Fanny Catteau

Multimodal focalization processes during French family dinners: a comparison between speaking and signing families

published or not. The documents may come

A set of intermodal prosodic markers

Introduction Family dinners

A privileged interactive situation as participants need to coordinate two kind of activities using one's mouth and hands : ✓ acting (eating, serving…) ✓ languaging (transmitting information to the other family members).

Our research questions

✓ How both adult and child dinner participants mark information salience in multiparty conversations, in which discourse topics are intermingled? ✓ How focus is shaped across modalities (audio-vocal and visuo-gestural), and which prosodic markers are used by '(gesturers-)speakers' and signers? Our hypotheses ✓ Focus markers are articulated in contrastive multimodal patterns ✓ A parallel can be drawn between 'spoken' French and LSF, in terms of the prosodic parameters and patterns used.

Background

Discourse combines shared information and new information (Lambrecht, 1994). New information is foregrounded, i.e. focused with a variety of processes including speech prosody (Rossi 1999, Di Cristo 1999, a.o.), sign prosody (Wilbur 1994 ;

Crasborn & van der Kooij 2013 a.o.) and (hearing-deaf) shared gestures (Ferré, 2003, a.o.).

In interaction, focus is implemented via multimodal phenomena: focalization is relatively well described in spoken dialogue, it remains understudied in signed interactions, and in both signed and spoken multiparty conversations.

Multimodal focalization processes during

••

  Amplified parameters value / Hyperarticulation / Contrastive patterns (including scansion)/ Markedness with (de)synchronisation and coarticulation Intermodal parameters for embodied languages Data: Family dinners Selected diners • 1 French speaking family (3 children = boys, 2, 3 and 9 y.o) • 1 LSF family (2 children = girls, 6 and 2,6 y.o) Selected sample • 30 minutes of each dinner • 60 utterances containing a focus DinLang (ANR-20-CE27-0010) • Families who primarily use French or LSF • Two wide-angle cameras (pilot study) + one 360°camera, one 360°sound recorder (DinLang setting) Relatively strong amplitude of the arm movement • Slight hold • Hyper articulated labialization LSF: SIGN-NAME(Ea) FORGET QUESTION SUNDAY!, KNOW GO SIGN-NAME(asso)!? Translation: 'Oh, Charlotte, I forgot, I have a question for you: on (next) Sunday, do you know who is coming to (the association) DLS?" LSF: PLEASE JUICE 'PEACH/APPLE', handshake IDIOT, PLEASE JUICE 'ORANGE/APPLE', knock-gesture PLEASE! JUICE! APPLE! gesture Translation: 'Please, some ape..., uh no, I'm stupid, please some apor... Ah! I'm really stupid, please some apple juice!!!' • Extended head nod • Extension of the bust • Manual hyper-articulation • Labial hyperarticulation (with vocalization [pɔm]) Prosodic focalization on "o": ▪ Syllable lengthening ▪ Pitch and intensity peaks ▪ Presence of a pause ▪ Glottal stop ▪ Hyperarticulation • Body markers: ▪ Head movements ▪ Right hand (iconic gesture) ▪ Eyebrows & mimic ▪ Speech synchronization • Prosodic focalization on each syllable: ▪ Syllable lengthening ▪ Pitch and intensity peaks ▪ Hyperarticulation ▪ Rhythmic scansion pattern • Body markers: ▪ Left arm movement ▪ Speech synchronization SPOKEN FRENCH : 'Non, c'est pas beaucoup, c'est pas beaucoup !' Translation: 'No it's not a lot ! It's not a lot !' SPOKEN FRENCH : 'Mais les ronds, c'était des "o" en fait !' Translation: 'But the circles were actually o's.'


Nino Tandilashvili

Factors Influencing Student Satisfaction in Higher Education. The Case of a Georgian State University 1

Keywords: service quality, customer perception, student satisfaction, HEdPERF, higher education

The quality of higher education has become an issue at governmental and international levels since the filed has been considered as a major asset for any nation's socio-economic and technological development. On the other side, the increased competition and globalization of education has pushed higher education institutions to compete for a better positioning on international markets of higher education. With a direct link between the service quality and students' satisfaction demonstrated in number of scientific research and public reports and debates, many agree on the importance of the quality management in higher education. Previous studies have identified positive correlation between the perceived quality of education and the degree of student satisfaction on the one hand, and on the other hand, between student satisfaction and their increased loyalty.

This study looks at the determinants of student satisfaction in one Georgian higher education institution. With the objective to detect the main components of service quality influencing students' satisfaction, the article uses HEdPERF as a measuring instrument of higher education service quality.

Data is collected from 793 students of different disciplines. An exploratory factor analysis identifies six factors for the further examination. After scale development, a multiple regression analysis is used to test the research hypothesis. The results of the study show that the administrative factors are the most sensitive to have influence on the students' satisfaction. Also, there is a positive relation between academic programs and student's satisfaction. A disproportionate attitude is observed between the importance of academic factors and satisfaction.

Introduction

Higher education, being one of the major assets for national development in the 21st century, has turned into an important topic of scientific research. On the one hand, number of studies have demonstrated a positive impact of the investment in higher education on country's economic 1 Tandilashvili, N. (2019) Factors Influencing Student Satisfaction in Higher Education. The Case of a Georgian State University, Conference proceedings, Research Association for Interdisciplinary Studies, DOI: 10.5281/zenodo.3267486 development [START_REF] Coleman | The Impact of Human Capital Measures on Firm Performance: A Comparison by Gender, Race and Ethnicity[END_REF]. On the other hand, authors observe an increase of competition among higher education institutions (HEI), due to some complementary factors: globalization, increased number of higher education institutions, modified funding schemes and global and national university rankings and league tables. These trends have pushed universities to "fight for their competitive advantages" on the global market (Quinn, Lemay, Larsen and Johnson 2009, 146), putting students' recruitment and retention in the top concerns of most university plans. In some countries student recruitment and retention are even imposed by government targets as poor retention rates have adverse funding consequences for institutions, like this is the case in the UK, for example.

The quality of education has become a concern at governmental levels as well. Number of national associations and organisations have been established with the unique aim to enhance the quality of services provided by local higher education institutions and thus, improve countries' competitive advantages. The European Association for Quality Assurance in Higher Education (ENQA), was established in 2000 in order to represent quality assurance and accreditation organisations from the European Higher Education Area and internationally. The aim of this organisation is to improve the quality of higher education among the Bologna Process countries by elaboration of set of standards, procedures and guidelines for higher education institutions of these countries. In the USA, there are some similar organisations out which the Council for Higher Education Accreditation (CHEA) is the most important. CHEA groups over 3,000 degree-granting colleges and universities and recognizes 60 institutional and programmatic accrediting organizations.

As a result of these recent developments, student satisfaction has become an extremely important issue for higher education institutions and their management. Previous studies have shown that HEI try to maximise students' satisfaction with their experience whilst they are at university and minimise dissatisfaction in order to retain them for future education [START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF]. Other studies also showed that HEI try to improve the institutions' performance across a number of league tables with the aim to improve students' external recruitment and internal retention rates [START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF][START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF]. Previous research has also demonstrated the need to study the topic within a national context due to cultural and socio-economic differences per country resulting in different factors influencing students' satisfaction and/or causing students' dissatisfaction.

The aim of the present research is to identify the factors that influence student satisfaction in Georgian higher education context. To do so a case study of one of the largest Georgian higher education institutions was realized. Quantitative data was collected using HEdPERF framework. Even though most of previous studies use SERVQUAL framework to assess students' satisfaction, some authors have found it "challenging" to use this universal instrument to accurately take into account the diverse nature of higher education field (Firdaus, 2005;Firdaus, 2005;Brochado & Cunha Marques, 2007).

HEdPERF framework was introduced as a superior instrument in terms of unidimensionality, reliability, and validity, which explained better the variance within the higher education setting in comparison to SERVPERF framework (Firdaus 2005).

The results of this research can help university management to improve the quality of academic and other services by providing a guidance framework that would allow focusing resources and efforts towards those areas that will improve most student satisfaction and reduce their dissatisfaction. At the same time, the research outcomes can enrich the scientific literature with two objectives: test the adequacy of the methodological tool HEdPERF in regards of the education industry and provide an empirical study in a post-soviet context where the topic of service quality, customer perception and student satisfaction are relatively poorly studied.

The article consists of three main parts. It stars with the review of a theoretical framework within which this study positions. It continues with methodological part explaining the research method used in this study and finishes with the discussion of the main findings.

Literature review

As mentioned above, the quality of education, student satisfaction and loyalty has been largely discussed and promoted in scientific literature, national debates as well as in strategies of higher education institutions since last couple of decades [START_REF] Gruber | Examining student satisfaction with higher education services: Using a new measurement tool[END_REF][START_REF] Temizer | Implementation of Student Satisfaction Index Model in Higher Education Institutions[END_REF].

In the scientific literature, the concepts of student satisfaction and loyalty are studied within the framework of customer satisfaction of service quality, i.e. a combination of emotional and cognitive reactions concerning a particular focus which takes place at a particular period of time (Giese and Cote 2000, 16).

Broadly speaking, customer satisfaction can be viewed from two basic constructs: a) satisfaction as meeting customers' expectations they had prior to purchase or after the use of products and services; and b) satisfaction as customers' relative perception of the performance of those products or services after using them. A customer's expectations about a product (service) tells how he/she anticipates product's (service's) performance. In this logic, satisfaction is a state that a human being experiences when he receives the product he expects [START_REF] Hasan | Service quality and student satisfaction: a case study at private higher education institutions[END_REF].

Authors suggest that there are different types of expectations when forming opinions about a product's anticipated performance. Day proposed four types of expectations customers have about a product: expectation about the costs, the product nature, the efforts in obtaining benefits and social value of the product [START_REF] Day | Consumer satisfaction, dissatisfaction and complaining behaviour[END_REF]. On the other hand, a perceived product performance is considered as an important construct of evaluating customer satisfaction, due to its ability to make comparisons with expectations.

In this sense, satisfaction is the perceived benefit minus the expected benefit of the product. When this equation is positive, we can talk about a satisfaction state (Grönroos 1984;[START_REF] Lewis | The Marketing Aspects of Service Quality[END_REF][START_REF] Smith | Script-based evaluations of satisfaction with services[END_REF].

The relationship between quality and satisfaction is not straightforward. Some early researchers argued that customer satisfaction is a precursor of service quality [START_REF] Bitner | The service encounter: diagnosing favorable and unfavorable incidents[END_REF]Parasuraman et al.1988) while others claimed that it is service quality that leads to customer satisfaction [START_REF] Hoisington | The loyalty elephant[END_REF][START_REF] Douglas | Someone is pulling the strings: Hypersensitive agency detection and belief in conspiracy theories[END_REF]. Applied to the context of higher education, the debate on the relationship between the quality of service and satisfaction of customers is even more sensitive than in other industries. First, there is an important debate on who is the customer of higher education institution.

For some, a customer of higher education is a student receiving the education (Hill 1995, 18). For others, academic staff, employers, and public sector should also be considered as customers of higher education [START_REF] Kanji | Total Quality Management in UK higher education institutions[END_REF] as they also (if not exclusively in some countries) pay for the service received by the students. Second, the relation between supplier-customer is not as clear in higher education as in other sectors [START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF]. Third, another debate around the student satisfaction concept is the viability to actually judge the quality of education. Some argue that a student is not capable of objectively judge the quality of education he receives as he is still under the process of forming skills and knowledge.

Putting these important topics aside in this research, the article continues with the assumption that knowing the factors that influence students' satisfaction can help higher education institutions improve their services (El-Hilali, Al-Jaber, Hussein 2014, 426) and/or create competitive advantages on the global marketplace. This topic is particularly important in a context of limited resources and strategic choices that HEI face (Firdaus 2005;[START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF]. The HEI management needs to make very delicate distinction between whether their objective of HEI should be to deliver satisfied customers, who will then develop a perception of high service quality, or that they should aim for high service quality as a way of increasing customer satisfaction.

Higher education service quality is a product of number of various services that students encounter within the HEI. Hill stated that such services are provided by administrators, teaching staff and managers as well as other HE employees [START_REF] Hill | Managing service quality in higher education: the role of student as primary consumer[END_REF]. Researchers perceive customer loyalty to be influenced by satisfaction, even if the structure of the relationship does not appear to be symmetric and linear [START_REF] Mittal | The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions[END_REF]. As explained by Kotler and Amstrong, a long-term relationship with a customer is based on satisfying his upmost values and that is why it is particularly difficult to achieve.

However, having loyal customer also benefits businesses financially in short and long term perspectives (Kotler and Amstrong 2015, 13). The same can be said about the cost of attracting new students and the benefits they can generate. DeShields, Kara, and Kaynak (2005, 128-139) illustrated that loyal students benefit universities not only with the tuition fees they pay, but also thanks to the reputation and the power of word of mouth promotion. Another advantage is that a satisfied student is loyal to his institution not only during, but also after his studies, and assist their alma mater in different ways [START_REF] Gibson | Measuring business student satisfaction: a review and summary of the major predictors[END_REF]. Douglas et al. (2006, 251-267) consider that the loyalty of a student can be reviled in different scenarios: decision to continue studies in the same institution; frequent usage of different services of the institution and recommending the institution to others. Thus, studying the satisfaction elements as well as those dissatisfying students is important step to assess the quality perception of university services. It can guarantee higher loyalty to HEI. The key question to start this study is thus, what are the factors that improve students' satisfaction. The next part of the literature review will investigate further this question.

Factors influencing student satisfaction

Previous studies have identified two sets of factors impacting the satisfaction level of students: factors that positively impact satisfaction and factors that decrease the satisfaction. The satisfier factors, on its turn, can be grouped within purely academic elements and elements that are linked with other services provided by higher education institutions. Some argue that these two sets of satisfier factors are linked and should be studied together. A recent study by [START_REF] Herdlein | Student Satisfaction, Needs, and Learning Outcomes: A Case Study Approach at a European University[END_REF] showed that students who are more involved in extra curriculum activities, such as sport event, organisational activities, social gathering, etc. are more satisfied compared to those who do not participate in any extra curriculum activities. According to Akil, 80% of students in Europe express the importance of these activities and often is the decision point when considering a future institution for their education.

European students also express higher satisfaction when they are confronted to a multinational learning environment (cited by [START_REF] Herdlein | Student Satisfaction, Needs, and Learning Outcomes: A Case Study Approach at a European University[END_REF].

Studies in European universities found that student satisfaction was largely influenced by the possibility given by higher education institutions to prepare them for a concrete profession, as well as their social, emotional and physical preparation [START_REF] Herdlein | Student Satisfaction, Needs, and Learning Outcomes: A Case Study Approach at a European University[END_REF]. Some students consider that the possibility to meet new friends and develop network for future is an essential function of a HEI [START_REF] Cook | Student Transition: Practices and Policies to Promote Retention[END_REF]. This social mission of modern university is also highlighted by other studies according to which, university campus should be planned in such a way to allow students live and study together and share knowledge and experience (Ping 1999, 16).

Student satisfaction seems to vary per discipline and student profiles [START_REF] Coates | Beyond Happiness: Managing Engagement to Enhance Satisfaction and Grades[END_REF]. For example, a study by Coates showed that the satisfaction level is relatively law among international students, also students who are aged 22 to 30 and students who study management, commerce and information technologies. The same study also showed that relatively higher satisfaction could be explained by good relation of students with academic and administrative staff and with other students [START_REF] Coates | Beyond Happiness: Managing Engagement to Enhance Satisfaction and Grades[END_REF]. A study by Marzo-Navarro et al. (2005, 53-65) also identified some demographic characteristics which explain the different level of satisfaction in the higher education field: age, gender, ethnic origin and level of education. Other studies showed that the satisfaction level may vary per religion and learning style in additional to some of the previously listed factors (Berg et al. 2010;Limon 2001).

One of the findings of previous studies suggested a positive relation between satisfaction and academic performance [START_REF] Rode | Life satisfaction and student performance[END_REF]. Some studies have shown that students with high performance (good grades) demonstrate more satisfaction compared to those with poorer academic results (Lo 2010). Studies have shown that students' academic performance largely depend on the learning environment they work in, such as small size classes, problem solving exercises (Johnson, Johnson & Smith 2007, 15-29). Positive environment motivates students to exceed their performance and makes them satisfied (Ocker and Yaverbaum 2001, 427-448).

There are number of factors that influence students' dissatisfaction instead of satisfaction. These factors are sometimes coming from outside the HEI, such as health issues, financial difficulties and family related problems [START_REF] Lemaro | Improving retention among college students: Investigating the utilization of virtualized advising[END_REF]. Others are directly linked with the institutions, like the perceived lack of quality of education, high tuition fees, disappointing learning environment and university reputation, inflexible schedule, bad location of intuition, etc. (Melinget al. 2012). Some studies showed that the level of dissatisfaction also depends on the little possibility of employment after the graduation [START_REF] Gbadosami | Specific remedy for specific problems: measuring service quality in south African higher education[END_REF] and lack of engagement in academic and nonacademic projects of their institutions (Garcia-Aracil 2009).

Previous studies have suggested that cultural and socio-economic differences may result in different perception and expectation of education quality. A study realized by LeBlanc and Nguyen 1997) in Canada showed that students' satisfaction depends on tuition fee and possibility of personal development in addition to the quality of service they receive. A study in Pakistan showed four priority services for the satisfaction in Pakistani higher education system: qualification level of academic staff, proposed curriculum, learning environment and classroom atmosphere [START_REF] Zaheer | A study examining the students satisfaction in higher education[END_REF]. Somewhat different results were founded in a study realised in Iran. Students' satisfaction in Tehrani universities seems to be impacted by factors such as the effectiveness of academic consultation, the services provided at the campus, student life, respect to ethnical diversity, security and possibilities to have financial support (Khosravi, Poushaneh, Roozegar and Sohrabifardd 2013).

HEdPERF tool of service quality evaluation in higher education field

The difficulty to evaluate the quality of service in general has been largely discussed in scientific literature [START_REF] Brady | Some new thoughts on conceptualizing perceived service quality: A hierarchical approach[END_REF]. There has been number of tools and models created with this purpose since the 1980s, like Service (perceived) quality model (Gronroos 1984); SERVQUAL [START_REF] Parasuraman | SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality[END_REF]); SERVPERF (Cronin & Taylor 1992); Three-component service quality model (Rust and Oliver 1994, 1-19); Multi-level model (Dabholkar et al. 1996, 3-16) and Hierarchical model (Brady and Cronin 2001, 34-49). These models are based on either qualitative, quantitative or mixed variables and apply to different industries. Each of these tools was created with an objective to more accurately judge the quality than the existing tools [START_REF] Othman | Adopting and Measuring Customer Service Quality (SQ) in Islamic Banks: A Case Study in Kuwait Finance House[END_REF]). SERVPERF, for example, was elaborated as an improvement of commonly used SERVQUAL as SERVPERF manages to identify more differences in the total result of a service quality evaluation (Cronin & Taylor 1994, 129) and prioritizes the perception over expectation (Boulding et al. 1993, 7-27). It has also been argued that an objective and in-depth evaluation of different service fields, requires models created exclusively for particular fields [START_REF] Vazirova | Measuring Service Quality in Higher Education: A Study of Post-Graduate Students in Northern Cyprus[END_REF]. With this aim, number of industry-based tools has been created. The table 1 lists these tools in a chronological order.

Table 1. Service quality evaluation models for different industries Researchers use either generic tools, like SERQUAL to evaluate higher education services, or apply industry-specific models, like HEdPERF. The later was originally created in 2003 and shaped in its current version in 2006. The main aim of the tool is to propose industry-based evaluation to the top management of higher education institutions. Firdaus (2005) proposed HEdPER in attempt to capture the "authentic determinants of service quality within higher education sector" (Firdaus, 2006, p.33).

The instrument was empirically tested for unidimensionality, reliability and validity using both exploratory and confirmatory factor analysis. Later studies confirmed that HEdPERF results were more accurate than other generic tools, like SERVPERF for example, when applied to higher education institutions (Randheer 2015, 35). The model views student perceptions of service quality as a six-factor structure consisting of 41 variables covering academic and administrative aspects of higher education. The variables are grouped into six main dimensions:

1. Non-academic aspectsall elements that are essential for the processes of education and are linked with the duties of non-academic staff of higher education institutions; 2. Academic aspectsthe competences of academic staff, their duties and responsibilities; 3. Reputationthe image of the institution; 4. Accessapproachability, ease of contact, availability and convenience of higher education institution; 5. Programme aspects -importance of offering wide range and reputable academic programmes with flexible structures and syllabus.

6. Understanding -understanding students' specific need in terms of counselling and health services (Firdaus 2006, 575).

As we have seen above, students perceive the quality of higher education service as a collection of different services [START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF][START_REF] Herdlein | Student Satisfaction, Needs, and Learning Outcomes: A Case Study Approach at a European University[END_REF]. HEdPERF tool allows identifying the variety of elements that can lead to higher satisfaction of students. The review of literature has shown the importance of considering students' experience in a higher education institution as a key issue for institutional strategy as satisfied students lead to higher financial and reputational benefits to HEI and allows them to more successfully position in an increasingly competitive marketplace. Thus the objective of the present research is to identify the elements that lead to students' satisfaction in a multidisciplinary higher education institution and provide recommendations to the management of HEI regarding the direction to allocate their resources and efforts.

Research methodology

The choice of Ilia State University as the case of our research was determined by its specific nature.

With 14 602 student, Ilia State University is one of the biggest higher education institutions in the Caucasus region. University was created 15 years ago after a merger of four half-century-old universities (created during the Soviet Union with the traditional soviet higher education culture).

Today university consists of four faculties: Faculty of Business, Technology and Education (45% of total students), Faculty of Arts and Science (37%), Faculty of Natural sciences and Medicine (8%) and the School of Law (10%), delivering education at bachelor, master and PhD levels. The four faculties have diverse history and traditions. Their unification under the same roof went relatively smoothly but did not end up in close relations. Students experience common institutional structure but have different disciplinary practices. Thus, with the selecting a multidisciplinary institution, we hoped to observe different perception of quality and satisfaction degree, as well as diversity of factors influencing satisfaction.

As our research emphasizes exploring commonalities in the study population, measurement and variables play an important role. A quantitative research approach was thus privileged in this study, using HEdPERF framework [START_REF] Abdullah | Measuring service quality in higher education: three instruments compared[END_REF]. In order to 1) refine the research hypothesis and 2) get indepth understanding of the research case, three focus groups were held with Ilia State University students and alumni. Each focus groups lasted one hour and a half. Ten participants of each group (students and alumni) were rigorously selected: representing all three levels of education (bachelor, master, and doctorate) and all four faculties of the institution.

The focus group meetings reviled some interesting aspects concerning the perception of quality of services received by students. On the positive side, the interviewees mentioned five elements: orientation of the university towards modern teaching trends; intra-organizational platform for lecturer-student interaction; professionalism of academic staff; possibilities to do exchange programmes; and large choice of study programs. On the other hand, the interviewees mentioned five negative points, like lack of student spaces (computer lab, after-school activities, etc.); curriculum of The survey was realized in 2018 during a one-month period. We selected a period when students were not busy with exams or any major curriculum activities. To guarantee the equal and independent representation for each element of the study population, we opted for random sampling method [START_REF] Kumar | Research Methodology: A Step-by-Step Guide for Beginners[END_REF]. We used disproportionate stratified sampling in order to assure the representation of all disciplinary fields present at the university without taking into consideration the size of each stratum [START_REF] Kumar | Research Methodology: A Step-by-Step Guide for Beginners[END_REF].

Our study population consists of 14602 students at bachelor, master and doctoral level. We received 793 questionnaires out of which 783 were retained for analysis, making 95% confidence limits of the normal distribution (mean difference ± 1.96 SD) and 3% margin error. Absolute majority of the study population is feminine (up to 80%). 296 respondents (37.8%) are under 20 years old. 409 represent the age group of 21-25 making the majority of the study population (51.9%). Only 61 students are 26-30 years old (7.8%) and 15 students between 31 and 35 (1.9%). Five students are over 36 years old (0.6%). 53,1% of respondents are employed and (7.8%) are self-employed against 40.1% unemployed population. The table 2 presents the student repartition per study level. The survey was distributed online to the entire student population with the help of university administration.

Table 2. Representation of study population per study level

Factor Analysis 39 variables were retained out of 41 HedPERF tool variables for this study. Two topics were rejected as had no link with the Georgian context 2 . The survey data was analysed with STATA software using factor analysis and regression.

Looking for grouping variables into conceptually similar and significant clusters, a first component analysis was performed using Promax rotation and Kaiser normalization. The method was privileged as the research aimed to identify a link between the independent variables. This step showed a strong positive correlation between HEdPERF variables and thus there was no need to additionally run Varimax method. For more reliability the choice was made to select only the factors with Eigenvalue higher or equal to 1 [START_REF] Kumar | Research Methodology: A Step-by-Step Guide for Beginners[END_REF]. Thus, the orthogonal analysis of 39 variables gave 3 main factors for further analysis as demonstrated in the table 3. Table 3. Factor analysis 2 Quality of Health service and sufficient and convenient time allocated to consultation are regulated nationally and does not change per institution. Also, these two variables were not at all discusses during the focus group meetings.

The three factors represent 13,37%, 11,96% and 11,74% of the whole dispersion, making up to 37,07%. The table 4 and the diagram 1 show the squared loading matrix which confirms our choice of the three first factors over the others, as after the factor 3, each successive factor is accounting for smaller and smaller amounts of the total variance (Pallant 2001, 154).

Table 5. Factor analysis of 39 variables

The factor analysis revealed that when it comes to administrative aspects, the most important issues for students are 1) effective and quick response from administration to their complaints (B11) and 2) the readiness of administrative staff to assist students (B12). Less important components seem to be 1) comfortable working hours of administrative staff (B16) and 2) confidentiality respect from the administrative staff (B20).

The quality of study programmes is assessed mainly by 1) large choice of study programmes and specializations (B24) and 2) the reputation of the offered programmes (B26). Less important for students while evaluating the quality of study programmes is the simplicity of employment after graduation (B29).

As for the quality of academic staff, students judge it vital that 1) academic staff care about them and show respect (B2) as well as 2) their readiness to assist students (B3). Surprisingly, the less important aspect to evaluate the quality of academic aspects is the in-depth knowledge of the topic and the capacity to answer students' questions (B1).

Correlation analysis

Multiple regression was used in this study to determine the overall effect of different service dimensions (independent variables) on the service quality level (dependent variable), and to assess the relative importance of the individual dimensions. This step of the research allowed us to investigate the main question of the research: the correlation between the quality university services perceived by the students, and the degree of their satisfaction.

As commonly acknowledged, correlation is valid only with 0,5 or higher significance level in social studies [START_REF] Kumar | Research Methodology: A Step-by-Step Guide for Beginners[END_REF]. In accordance with the first two steps of our research, the first three factors showed the highest correlation level as demonstrated in the table 6.

Table 6. Correlation analysis of the top ten factors

The research fully confirmed hypothesis 5, 6, 7 and 8 and partially confirmed hypothesis 1 and 3.

Hypothesis 2 and 4 were not confirmed within this study. The correlation analysis confirmed that purely academic factors are as important as non-academic elements like the quality of administrative staff (H5, H6) and reputation of study programmes (H1). Other elements, like study environment and extra curriculum activities and spaces also seem as major elements in overall quality perception (H8).

Among the non-academic services (H6), the quality of administrative staff seems to be the most important factor while evaluating the overall university quality. Factor B11 "effective and quick response to students' complaints" plays the major role in evaluation of the administrative staff quality.

However, the students seem to be the least satisfied with this component. Similarly, factor B12 "readiness of administrative staff to assist students" is second major element composing the quality of administrative staff. However, our study showed that students are not absolutely satisfied with this component. An opposite correlation was observed regarding the factor B20. Students are particularly satisfied with the "confidentiality respect from the administrative staff" but for them this is not the key element to judge the quality of university administration.

Figure 1. Correlation between the perceived quality of administrative aspects and the level of satisfaction Opposite result was observed at the level of academic factors (H5). Even if the studied population is particularly satisfied with the factor B1 "in-depth knowledge of the topic and the capacity to answer students' questions", for them this element is not essential while evaluating the quality of university services. This result coincides with the literature review where we observe the increase of importance of non-academic factors vis-à-vis academic considerations. As for the academic programme factor (H1), we observe a positive correlation between the perception of quality and the level of satisfaction. Similarly, a positive relation is observed between the "dissatisfaction" in terms of "simplicity of employment after graduation" (B29) and little importance of this variable in overall value of factor evaluation. Only separate variable in the factor 2 is the B27 "the reputation of study programmes". For the respondents, the university programmes have good reputation in the society, but it is not important aspect for them to judge the quality of study programmes. Table 7. Difference of satisfaction between experienced and first-time students

The hypothesis 7 on a positive correlation between satisfaction and loyalty is confirmed within this study. Students who are particularly satisfied (overall satisfaction higher than 3,8) are ready to continue studies at Ilia State University in future. This result shows the high expectations from students already observed at focus group meetings. Only those who judge that the quality of services proposed by the university as very good (>4.0000) are loyal to the institution. Receiving "good" services (3.000) is not enough to be loyal to their HEI.

Table 8. Satisfaction and loyalty correlation analysis

The present study shows that average satisfaction level is not enough to guarantee students' loyalty in today' very competitive education market. This result correspondents to foreign experiences. Previous research by [START_REF] Blackmore | Measuring student satisfaction at a UK university[END_REF] found that even whilst satisfaction ratings overall were at an acceptable level, a significant number of respondents claimed that they would not recommend their institution to others.

Our study showed that academic elements, such as the quality of academic staff and the courses are very important component of HEI service quality. However, the quality of administrative staff is even more important as it is administrators with whom students deal at daily basis for variety of services. Study programme reputation is another important element. However, the respondents distinguish clearly that the reputation itself cannot guarantee the quality of the programme. For them study environment (like existence of free spaces for their academic and non-academic activities) is crucial.

Conclusion

This paper outlines the findings of a study involving bachelor, master and doctorate level students within one Georgian university. The study showed that higher education service quality results in a combination of different elements out of which the quality of administrative staff is the most important, followed closely by the quality of academic staff. The reputation of study programmes and available spaces for students are other essential elements.

Even if the overall satisfaction seems quite high in this study, respondents are not necessarily ready to recommend their institution or to remain loyal to it, unless they are particularly satisfied with the service quality. This observation corresponds to the findings of previous studies showing the importance of customer-orientation in increasingly competitive field of higher education. From management point of view there are obvious areas of interest where deploying resources and efforts should be targeted. These are termed as the critical areas, i.e. those that are influencing loyalty behaviours.

Evaluating service quality level and understanding how various dimensions impact overall service quality would enable HEI to efficiently design the service delivery process. In addition, knowing the strengths and weaknesses of the dimensions which influence the satisfaction level of students can result in better allocation of resources so as to provide a better service to students.

There are a number of limitations with this study. First, as the study population was selected randomly in order to guarantee an objective representation of each element, an uneven representation was observed for some variables. An absolute majority of respondents are female (86%). Similarly, students who have no experience of studying in more than one HEI represent a large majority (78% of the population).

Future steps of this research can be undertaken in order to compare the results of this study to an international counter party. It will be interesting to observe similar trends over cultural and socio-demographic varieties.

  study programs; less flexible study timelines; technical issues with the intranet during the online selection of courses; and lack of employment possibilities. Results of the focus group meetings and the review of literature allowed posing the following hypothesis for this research:  Hypothesis 1: Positive reputation of study programmes increases students' satisfaction;  Hypothesis 2: There is a different satisfaction level among different discipline students;  Hypothesis 3: There is a different satisfaction level per age group;  Hypothesis 4: Students with no experience of other higher education institutions are more satisfied than those with experiences;  Hypothesis 5: Perceived quality of academic aspects explains the satisfaction level;  Hypothesis 6: Non-academic services are as important as academic aspects for students' satisfaction;  Hypothesis 7: Satisfied students are more loyal to HEI;  Hypothesis 8: Environment and location of university explains students' satisfaction.

  that, in accordance to the Promax rotation and Kaiser normalization tests, the first three factors consist of the highest number of variables. The factor 1 regroups variables B9 to B22 and concerns administrative services of the university. The second factor regroups variables B23 to B27 and B29 and concerns the curriculums and competitiveness of study programs, as well as student employability issues. The third factor contains first eight variables and concerns the academic aspects. The factors were named accordingly: Factor 1 -Administrative aspects, Factor 2 -Study programmes and Factor 3 Academic staff. We only retained factors with more than three variables for the further steps of the analysis (Cohen, Manion and Morrison 2007).

Figure 2 .

 2 Figure 2. Correlation between the perceived quality of academic staff and the level of satisfaction

Figure 3 .

 3 Figure 3. Correlation between the perceived quality of study programmes and the level of satisfaction