With recent advances in wireless and portable neurotechnologies, new applications of brain-computer interfaces (BCI) have emerged. Previously, BCIs were mostly targeted towards communication and rehabilitation applications, such as powered wheelchair control or so-called brain spellers [1, 2]. Today, other types of applications have emerged and passive BCIs have been developed to measure implicit information from the users, such as their mental states (e.g., stress level), fatigue levels, and more recently, their mood and emotional states [3]. These latter are referred to as affective BCIs (aBCI). Representative applications of passive and affective BCIs include neurogaming [4, 5], neuromarketing [6], attention monitors [7], and automated multimedia affective tagging [8], to name a few. In this paper, we explore a new application for aBCIs: monitoring the human influential factors needed in quality-of-experience (QoE) perception models used by telecommunication service providers.

QoE has been formally defined as ‘the degree of delight or annoyance of the user of an application, resulting from the fulfillment of his/her expectations in light of the user’s personality and current mental state’ and is driven by three key influence factors: technological, contextual, and human [9, 10]. Technological influence factors (TIFs) refer to system and network parameters that can be readily measured (e.g., delay, bitrate). Contextual influence factors, in turn, can describe the user’s environment, as well as economic aspects (e.g., pricing, churn rate). Lastly, human influence factors (HIFs) characterize the user’s perception, emotional and mental state with respect to a service [9, 10]. For much of the last decade, experts have advocated for QoE to be used as the standard user-centric quality metric for emerging applications and products [11]. Notwithstanding, the majority of existing work has focused only on technological and contextual aspects [12, 13]. In order to develop true QoE assessment methods, however, HIFs also need to be incorporated. In this paper, we propose the use of aBCIs during speech QoE perception tests to measure such HIFs.

User affective states can be inferred from multiple sources, such as facial expressions [14], body posture [15], and even voice [16]. These behavioural cues, however, can be concealed by the user. As such, monitoring of neurophysiological sources, such as heart rate, skin conductance, or neural responses, have become popular as they also accurately characterize human emotional states, but are more difficult to be volitionally concealed. Neurophysiological tools, such as electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) have been used in the past for affective state characterization with varying degrees of success [1720], as well as for QoE assessment [2123]. While EEG relies on measuring the electrical activity in the brain with high temporal precision (order of milliseconds), it suffers from limited spatial resolution. Functional NIRS, on the other hand, tracks cerebral hemodynamics with better spatial resolution than EEG, but with relatively poor temporal precision (order of seconds) [24]. Overall, EEG-based aBCIs have been more widely used and reported in the literature, but fNIRS is quickly gaining grounds [25].

Here, we are interested in exploring the use of EEG based aBCIs to measure HIFs to be used in objective models of speech QoE perception. Two case scenarios based on emerging speech applications are explored, namely text-to-speech (TTS) systems and hands-free communications. Over the last few years, TTS systems have gained tremendous popularity, particularly in the domain of personal digital assistants (e.g., Apple’s Siri, Google Now, and Microsoft’s Cortana), automated call centres, reading assistants to the blind, and global positioning systems. Moreover, hands-free technologies have also gained popularity due to emerging voice-controlled consumer electronics, teleconferencing, speech/speaker recognition, and automatic meeting transcription applications, to name a few. These applications were selected for two main reasons: (1) despite their advances, degradations incurred on the speech signal can severely hamper the user’s perceived QoE (e.g., choppy signal in concatenative TTS systems [26] or ambient noise and room reverberation in hands-free applications) and (2) significant effort has been placed to develop objective QoE perception models based solely on technological and contextual influence factors, thus allowing for the characterization of the importance of HIFs on ‘true’ QoE perception modelling. Experimental results with data collected from two QoE perception studies show the importance of aBCIs and of HIFs on QoE perception modelling.

The general scheme of the proposed aBCI system for user QoE perception monitoring is shown in Fig. 1 (for illustration purposes, a TTS example is shown). Within this framework, the audio signal (e.g., synthesized speech signal generated by a personal digital assistant) is used to extract TIFs and is presented to participants, whom in turn are wearing an EEG-based aBCI. Features from the EEG signal are then extracted and used as HIF correlates. The TIF and HIF parameters are then input to a QoE model which outputs an estimated user-perceived QoE value. As can be seen, the proposed setup, without loss of generality, does not investigate the effects of contextual factors on QoE; such analysis is left for future study. To the best of our knowledge, this is the first time that (1) HIFs are quantitatively shown to be important influence factors for QoE measurement, (2) EEG-based aBCIs are used to objectively monitor HIFs for QoE modelling, and (3) the developed QoE models are validated on two independently acquired data sets.

Fig. 1
figure 1

An overview of the aBCI approach for monitoring user QoE

The remainder of this paper is organized as follows. “Methods and materials” section provides an overview of the methodology and experimental setups used. “Experimental results” section and “Discussion” section describe the experimental results and discussion, respectively. Lastly, conclusions are presented in “Conclusion” section.


Speech QoE can be assessed either subjectively or objectively [12, 27]. Subjective testing typically involves user interviews, ratings and surveys to obtain insights about the end-user’s perception, opinion and emotions about speech quality and their overall experience, thus forming the ‘ground truth’. Objective assessment, on the other hand, replaces the listener with a computational algorithm that has learned complex mappings between several key factors and previously-recorded subjective ratings. Existing objective methods have been “technology-centric”, thus relying mostly on technological and contextual factors [12]. In order to develop QoE assessment methods, however, human influential factors also need to be incorporated. More recently, neurophysiological monitoring tools have been used to develop objective models which try to estimate the ground truth. Having this said, in this section we describe the methodology and experimental setup used in our study.

Subjective assessment methods

Quantitative subjective assessment methods typically involve the construction of questionnaires with rating scales, surveys, and user studies which can be conducted either in laboratory or “real-world” settings. The International Telecommunications Union (ITU), for example, has developed subjective study guidelines for perceptual speech quality evaluations. Recommendation P.800 [28] describes how to conduct the widely-used mean opinion score (MOS) listening test. The human-computer interaction domain has also covered guidelines on subjective testing methods for speech interface quality evaluation [29]. For speech intelligibility assessment, subjective tests are conducted that explore syllable, word, or sentence recognition.

In the affective computing domain, in turn, human affect is considered to manifest itself through multifaceted verbal and non-verbal expressions. Therefore, one common approach is to categorize affective factors using two broad dimensions comprising valence (V) and arousal (A) on two-dimensional plots [30]. Valence refers to the (un)pleasantness of an event, whereas arousal refers to the intensity of the event, ranging from very calming to highly exciting [31, 32]. Using the valence-arousal (VA) model, various emotional constructs have been developed, as depicted by Fig. 2 [30, 32, 33]. In order to quantitatively characterize these two emotional primitives, the Self Assessment Manikin (SAM) pictorial system is commonly used, as shown in Fig. 3 [30, 32]. As can be seen, the SAM for valence ranges from a smiling, happy manikin to a frowning, unhappy one. For arousal, in turn, SAM ranges from very excited, eyes-open manikin to a sleepy, eyes closed one [34]. It is important to emphasize that a third dimension, dominance, has also been proposed and refers to the controlling/dominant nature of the felt emotion [31]. While dominance has shown to be useful in characterizing emotions felt by subjects viewing pictures [17] and watching movies [35], it has shown limited use with speech stimuli, thus is omitted from our studies.

Fig. 2
figure 2

Two-dimensional Valence-Arousal (VA) emotion map with representative emotions

Fig. 3
figure 3

SAM scales for Emotion Assessment; top: Arousal; bottom: Valence

Objective assessment methods

Objective assessment methods are also often referred to as instrumental measures. QoE insights are normally estimated either using technology-centric speech metrics or, more recently, via neurophysiological monitoring tools (i.e., aBCIs), as detailed below.

Technology-centric speech metrics

Technology-centric models replace the human rater by a computer algorithm which has been developed to extract relevant features from the analyzed signal (speech, audio, image, or video) and map a subset/combination of such features into an estimated QoE value. For speech technologies, models can be further categorized as full-reference (also known as double-ended, intrusive) or no-reference (single-ended, non-intrusive), depending on the need, or not, of a reference signal, respectively. The ITU, for example, has standardized several objective models over the last decade, such as PESQ (recommendation P.862 [36]) and POLQA (recommendation P.863 [37]) as full-reference models and ITU recommendation P.563 [38] as no-reference.

For hands-free speech communications, one non-intrusive method called reverberation to speech modulation energy ratio (RSMR) has been shown to outperform the abovementioned standard algorithms, thus will be used in our studies. A description of the metric is beyond the scope of this paper and the interested reader is referred to [39, 40] for more details. Moreover, for TTS systems, studies have shown the importance of signal-based metrics [41], such as prosody and articulation [42]. Recently, two quantitative parameters were shown useful [42], thus are used in our TTS study: the slope of the second order derivative of the fundamental frequency (\(sF0''\)) and the absolute mean of the second order mel frequency cepstrum coefficient (\(MFCC_{2}\)). While the \(sF0''\) feature models the macro-prosodic or intonation-related properties of speech, \(MFCC_{2}\) models articulation related properties [42]. In our experiments, the openSMILE toolbox [43] was used to extract these features using the default window length of 25 ms and frame shift of 12.5 ms.

aBCI features

Typical EEG-aBCI features involve the calculation of specific EEG frequency subband powers, such as delta, theta, alpha, beta, or gamma sub-bands, as well as their interactions [44]. To characterize human affective states, the human prefrontal cortex (PFC) region has been widely used. Seminal studies have shown differential involvement of right and left hemispheres in emotional processing, where the right hemisphere is linked with unpleasant emotions and the left with pleasant emotions [45, 46]. As such, an asymmetry index has been developed which measures the difference in EEG activity in the alpha band (8–12 Hz) from the left to the right hemisphere; the index has been shown to be correlated with the valence emotion primitive [47, 48]. Moreover, the beta frequency band (12–30 Hz) power at the medial prefrontal cortex (MPC) has been associated with arousal [49].

Therefore, in order to objectively characterize affective factors, two features were extracted, namely an alpha-band asymmetry index (AI) and the MPC beta power (MBP), as correlates of valence and arousal, respectively. More specifically, the AI feature was computed as the difference between the natural logarithm of the alpha power of the left (\(\alpha _{AF3}\)) and right frontal electrodes (\(\alpha _{AF4}\)), as highlighted in the electrode map depicted by Fig. 4 and suggested by [47]:

$$\begin{aligned} AI=\ln (\alpha _{AF4})-\ln (\alpha _{AF3}). \end{aligned}$$

The MBP feature, in turn, was computed as the beta-band power in the AFz position (central electrode highlighted in Fig. 4), as suggested by [17, 50].

Fig. 4
figure 4

EEG electrode placement following the 10–20 International System. Highlighted electrodes were used in the calculation of the asymmetry index (AF4 and AF3) and in the MPC beta power (AFz) feature

Experimental setup: dataset 1 (hands-free communications)


Fifteen naive subjects participated in this study (eight female, seven male; mean age = 23.27 years; SD = 3.57; range = 18–30); all of them were fluent English speakers (participants for whom their first or second language was English). All participants reported normal auditory acuity and no medical problems. Participants gave informed consent and received monetary compensation for their participation. The study protocol was approved by the Research Ethics Office at INRS-EMT and at McGill University (Montreal, Canada).


As stimulus, a clean double-sentence speech file created from the TIMIT database [51] was used. The clean file was then convolved separately with room impulse responses typical of three practical environments. The first represented a living room environment with a reverberation time (RT) of approximately 400 ms. The second represented a classroom environment (\(RT=1.5\) s) and the third a large auditorium (\(RT=2\) s). Higher RT values indicate rooms with greater reverberation levels, which in turn, are more detrimental to perceived speech quality. For consistency, all files were normalized to \(-\)26 dBov using the ITU-T P.56 voltmeter [52]. The sentence was uttered by a male speaker and digitized at 8 kHz sampling rate with 16-bit resolution. Speech files representative of the four hands-free conditions were presented to the participants over several trials, as detailed in the sections to follow. More details about this database can be found in [53].

Experimental protocol

The experiment was carried out in two phases. In the first phase, participants were asked to fill a demographic questionnaire and to report their perceived QoE for each file using a 5-point MOS scale (1 = bad, \(\ldots\),5 = excellent), as well as their perceived arousal and valence affective states using a continuous 9-point SAM scale, as shown in Fig. 3. Stimuli were repeated thrice for each speech quality condition. Whereas in the second phase, participants were placed in a listening booth and 64-channel EEG data was collected using an Active II Biosemi device with electrodes arranged in the modified 10–20 standard system (see Fig. 4). Four electrodes for electro-oculography (EOG) and two mastoid electrodes (right and left) were used for reference. The test consisted of an oddball paradigm, where the clean speech served as the standard stimulus and the reverberant files served as deviants. Clean and reverberant speech files were delivered in a pseudo-randomized order, forcing at least one standard stimulus to be presented between successive deviants, in sequences of 100 trials. Stimulus was presented with an inter-stimulus-interval varying from 1000 to 1800 ms. Participants were seated comfortably and were instructed to press a button, whether they detected the clean stimulus or one of the deviants. Stimulus was presented binaurally at the individual’s preferred listening level through in-ear headphones.

Experimental setup: dataset 2 (TTS systems)


Twenty-one fluent English speakers (eight females) with average age 23.8 (\(\pm\)4.35) years were recruited for the study. None of them reported having any hearing or neuro-physiological disorders. Insert earphones were used to present the speech stimuli to the participants at their individual preferred volume levels. The study protocol was approved by the INRS Research Ethics Office and participants consented to participate and make their de-identified data available freely online. The participants were also compensated monetarily for their time.


Table 1 lists the speech stimuli used for this study along with certain important aspects. The stimuli consisted of four natural voices and seven synthesized voices, obtained from commercially available systems, namely: Microsoft, Apple, Mary TTS Unit selection & HMM, vozMe, Google and Samsung. Tested systems cover a range of different concatenative and hidden Markov model (HMM) based systems. A non-identifying code is provided for each of the seven TTS systems in Table 1. Speech samples were generated from two sentence groups (A and B), each comprising four sentences. Thus, the total number of stimuli used in this study were forty-four [(4 natural voices + 7 synthesized voices) \(\times\) 4 sentence sets]. The speech stimuli consisted of both male and female voices for five of the seven TTS systems. The speech stimuli were presented to listeners at a sampling rate of 16 KHz and a bitrate of 256 kbps. Table 1 also details the duration range of the speech stimuli for each system. More details about this database can be found in [54].

Table 1 Description of the stimuli used for the listening test in dataset 2

Experimental protocol

The experimental procedure was carried out in accordance with ITU-T P.85 recommendations [55]. Participants were first comfortably seated in front of the computer screen inside a soundproof room. Participants were then fitted with 62 EEG electrodes (AF7 and AF8 were not used) using a compatible EEG cap. Insert earphones were placed comfortably inside the participants’ ears to deliver the speech stimuli. The experiment was then carried out in two phases: a familiarity phase and an experimental phase. In the familiarity phase, participants were presented with a sample speech file followed by the series of rating questions, thus illustrating the experiment procedure and giving them the opportunity to report any problems and/or concerns. Next, the experimental phase consisted of several steps as shown in Fig. 5. First, data from a baseline period was collected for 1 min in which the participants were advised to focus only on the cross bar in the middle of the screen and not think about anything else. This was followed by a 15-s rest period followed by the presentation of randomized speech stimuli, one sentence set (approximately 20 s long) at a time. The rest period was provided to allow neural activity and cerebral blood flow to return to baseline levels prior to TTS stimulus presentation. Moreover, following each stimulus participants were presented with rating questions on the screen where they scored the stimulus using a continuous slider on the 5-point MOS scale and the 9-point SAM scales for valence and arousal. This rest-stimulus-rating combination is referred to as an experimental ‘block’. The procedure is repeated 44 times, where each block corresponds to one of the 44 speech stimuli available in the dataset.

Fig. 5
figure 5

Visual representation of the protocol used in the experimental phase for Dataset 2. The baseline period collected at the beginning and at the end of the test extended for 60 s. The pre-stimulus rest period lasted 15 s and the average length of the speech stimulus used was 20 s. The time allotted for rating the subjective dimensions varied per stimulus and was participant-dependent. The rest-stimulus-rating combination comprised an experimental block and a total of 44 blocks were used

EEG data processing

For data analysis, the MATLAB-based EEGLAB toolbox was used [56]. Data was recorded at 512 Hz but down-sampled to 256 Hz and band-pass filtered between 0.5 and 50 Hz for offline analysis. All channels were re-referenced to the ‘Cz’ channel. For the first dataset, continuous EEG data were divided into epochs of 3000 ms, time locked to the onset of the stimuli with a 200 ms pre-stimulus baseline. For the second dataset, the EEG data was divided into epoch-length corresponding to the speech stimulus length with a 300 ms pre-stimulus baseline. In order to remove artefacts from the EEG signals (e.g., eye blinks), a combination of visual inspection and independent component analysis was performed. Features were then extracted from the artefact-free segments.

QoE model performance assessment

In order to assess QoE model performance, three tests were conducted for each study. First, we explored the goodness-of-fit (\(r^2\)) achieved by using only the technology-centric speech metric as a correlate of the QoE score reported by the listeners (denoted as \(QoE_{Tech}\)). Second, we investigated the gains obtained by including HIFs into the QoE models. Here, we measured the \(r^2\) obtained from a linear combination of the technology-centric speech metric combined with the subjective valence and arousal (‘ground truth’) ratings reported by the listeners (denoted as \(QoE_{HIF}\)). Gains in the goodness-of-fit metric should indicate the benefits of including HIFs into QoE perception models. Lastly, we replaced the ground truth HIFs by the aBCI features that are used as correlates of the listener’s emotional states (denoted as \(QoE_{aBCI}\)). It is expected that the \(r^2\) achieved will lie between those achieved without and with HIFs, thus signalling the importance of aBCIs in QoE perception modelling.

Towards this end, the goodness-of-fit measures were obtained by developing linear regression equations for each of the three proposed tests (\(i=1,\ldots ,3\)). Linear regression model ‘i’ had dependent variable \(y_{i}\) as a linear combination of ‘p’ independent variables (or regressors, \(x_{ip}\)) weighted by regression coefficients (\(\beta _{p}\)) and error (\(\epsilon _{i}\)). The linear regression is formulated as follows:

$$\begin{aligned} y_{i} = \epsilon _{i} + \beta _{1}x_{i1} + \beta _{2}x_{i2} ... + \beta _{p}x_{ip} = \varvec{x^{T}_{i}\beta } + \epsilon _{i} . \end{aligned}$$

The values of \(\beta\) and \(\epsilon\) are estimated using least squares fitting on training data.

Experimental results

In this section, we report the experimental results obtained from the subjective and objective methodologies used.

Dataset 1: hands-free communications

Subjective data evaluation

At first, the impact of RT over human QoE factors was analyzed by computing descriptive statistics, as shown in Fig. 6. The obtained quality and affective ratings (valence and arousal) were averaged over all participants. As expected, a monotonic decrease across all subjective factors was observed with an increase in reverberation time. In order to test the effects of the four speech quality conditions on perceived quality, a repeated measures ANOVA with a Greenhouse-Geisser correction was used. A significant main effect was found (\(F(df_{1} = 1.08, df_{2} = 15.16) = 240.692; p\le 0.05\)) with effect size, \(\eta ^2 =0.945\), thus indicating significant between-group variations in QoE-MOS ratings for the four tested conditions. Moreover, post-hoc pairwise t-test comparisons with Bonferroni correction showed QoE scores to significantly decrease for each of the four tested conditions.

Fig. 6
figure 6

Box plots for the subjective overall impression (QoE-MOS), valence and arousal scores. The figure shows the box plots for subjective ratings collected from Dataset 1. The red line in the middle of the box plot shows the median for a particular rating whereas, the red cross bars show the outliers

Similar analysis was performed for the arousal and valence ratings. For arousal, statistical difference across four condition groups was found (\(F(1.05,14.78)=11.83; p\le 0.05; \eta ^2 =0.458\)), as was the case with valence (\(F(1.08,15.20)=91.85; p\le 0.05; \eta ^2 = 0.868\)). The stronger effect (\(\eta ^2\)) seen for valence over arousal suggests that RT has a stronger influence on the perceived pleasantness of the experienced files. Post-hoc pairwise t test comparisons with Bonferroni correction were also computed for the two emotional primitives. It was found that valence ratings significantly decreased with increasing RT levels (\(p\le 0.05\)). For arousal, on the other hand, significant differences were not seen between the \(RT=1.5\) s and \(RT=2\) s pairs, suggesting only subtle differences in arousal between the two conditions.

To better understand the impact of reverberation time on users’ emotional ratings, the 2-dimensional valence-arousal map can be used, as depicted by Fig. 7. In the plot, the x-axis represents the SAM scores for valence and the y-axis represents arousal. The data are centred at (5,5), which is the neutral state according to the 9-point SAM scale. The positive valence and high arousal (PVHA) quadrant represents emotions such as happiness, excitement, and alertness. The PVLA quadrant normally represents emotional characteristics like satisfaction, relaxation, and content. The negative valence and high arousal (NVHA) quadrant, in turn, represents emotional characteristics such as agitation and anger. Affective behaviors such as boredom, fatigue, discomfort, and dissatisfaction are represented in the NVLA quadrant. As can be seen, for the clean signal the majority of the participants rated the stimulus between 4 and 6 in the arousal and valence scales, thus corroborating the neutrality of the speech content. As reverberation levels increase, the majority of participants rated between 2 and 4 in the arousal and valence scales, thus, pointing towards the (NVLA) quadrant states such as discomfort, unpleasantness, and boredom.

Fig. 7
figure 7

Subjective Valence vs. Arousal emotional map across the four tested conditions in Dataset 1. The figure shows the spread of users’ valence and arousal scores, in response stimuli used in Dataset 1, on a valence-arousal map

Pearson correlations between RT and each of the three subjective factors were also computed and are reported in Table 2. All correlation coefficient values were found to be significant (\(p<0.05\)) with quality and valence ratings showing the strongest (positive) correlations with each other and strong (negative) correlations with RT. On the other hand, arousal showed only a mild correlation with quality and valence.

Table 2 Pearson correlation analysis between the three subjective factors and reverberation time (RT) for dataset 1

Objective model evaluation

As mentioned in “QoE model performance assessment” section, three QoE models were implemented in order to gauge the benefits of including HIFs, as well as aBCI features into the equation. For this study, the following QoE models were found:

$$\begin{aligned} QoE_{Tech}\,= {} 0.74-0.5\times RSMR, \end{aligned}$$
$$\begin{aligned} QoE_{HIF} = {} 0.38-0.27\times RSMR+0.73\times Val-0.13\times Ar, \end{aligned}$$
$$\begin{aligned} QoE_{aBCI} = {} 0.02+0.20\times AI-0.07\times MBP-0.94\times RSMR, \end{aligned}$$

where ‘Val’ and ‘Ar’ indicate the valence and arousal subjective ratings, respectively, and the \(\beta\) and \(\epsilon\) parameters were obtained on a subset of the available data. The obtained goodness-of-fit (\(r^2\)) value for (3) was 0.72 with a root mean squared error (RMSE) value of 0.135. The \(r^2\) value for (4), in turn, was 0.87 with RMSE value of 0.093, thus suggesting the importance of HIFs in QoE perception assessment models. Lastly, for (5) an \(r^2\) value of 0.81 and an RMSE of 0.097 was obtained, thus signalling the benefits of using aBCIs for the task at hand. When comparing the output of the objective QoE model in (5) and model in (4), a Pearson correlation coefficient of 0.90 was obtained.

Dataset 2: TTS systems

Subjective data evaluation

Initially, the impact of varying TTS system quality on human QoE factors was analyzed by computing descriptive statistics, as shown in Fig. 8. It was observed that systems 1 and 3 showed the highest quality ratings, which can be expected as both corresponded to natural voices. However, the other two natural voice systems (2 and 4) were rated at medium quality levels. This was due to the fact that the speaker used for system 4 was specifically asked to speak with a neutral intonation and listeners reported voice 2 as sounding breathy, thus lower in quality than the other natural voices. Regarding the TTS systems, system 11 scored the least in terms of quality, valence and arousal. In general, the synthesized speech systems scored lower than natural systems. However, comparing the systems which used synthesized voices, system 5 scored the maximum in terms of quality and valence. In order to test the effects of these speech systems in terms of perceived QoE, an ANOVA was used. A significant effect was found \(\left[ F(10,913)=143.32; p\le 0.01\right]\). Moreover, post-hoc pairwise t-test comparisons with Bonferroni correction showed QoE-MOS scores to significantly differ between natural voices and TTS system outputs.

Fig. 8
figure 8

Box plots for the subjective overall impression (QoE-MOS), valence and arousal scores. The figure shows the box plots for subjective ratings collected from Dataset 2. The red line in the middle of the box plot shows the median for a particular rating whereas, the red cross bars show the outliers

Similar analysis as above was performed for the arousal and valence ratings. For valence, statistical difference across eleven condition groups was found \(\left[ F(10,913)=96.28; p\le 0.01\right]\), as was the case with arousal \(\left[ F(10,913)=31.5; p\le 0.01\right]\). The stronger F-statistic seen for valence over arousal suggests that synthesized speech quality has a stronger influence on the perceived pleasantness of the experienced files. Post-hoc pairwise t-test comparisons with Bonferroni correction were also computed for the two emotional primitives. It was found that valence and arousal ratings significantly differed between the natural and synthesized voices. Moreover, to better understand the impact of TTS system quality on users’ emotional ratings, the 2-dimensional valence-arousal map was used, as depicted by Fig. 9. It can be seen that the natural voice cases were present mostly in the PVHA quadrant of the valence-arousal map, whereas all synthesized voices existed in the NVLA quadrant. Furthermore, a comparative analysis of subjective dimensions between male and female voices, using ANOVA, indicated a significant difference for male listeners, where the male listeners rated QoE-MOS and Valence for male voices higher than female voices with \(F(1,258)=15.72; p\le 0.01\) and \(F(1,258)=6.49; p\le 0.05\), respectively. Previous research has found similar preference of male voices over female voices, for male listeners [57], and, male and female listeners [58].

Fig. 9
figure 9

Subjective Valence vs. Arousal emotional map across the 11 tested conditions in Dataset 2. The figure shows the spread of users’ valence and arousal scores, in response to stimuli used in Dataset 2, on a valence-arousal map

Lastly, Pearson correlations between each of the three subjective factors were also computed, as reported in Table 3. All correlation values were found to be significant (\(p<0.05\)) with quality and valence ratings showing the strongest (positive) correlations with each other. Arousal, on the other hand, showed only a mild correlation with quality and valence.

Table 3 Pearson correlation analysis between the three subjective factors for dataset 2

Objective model evaluation

As mentioned in “QoE model performance assessment” section, three QoE models were implemented in order to gauge the benefits of including HIFs, as well as aBCI features into the equation. For this study, the following QoE models were found:

$$\begin{aligned} QoE_{Tech} = {} 0.36-0.56\times MFCC_{2}+0.44 \times sF0'', \end{aligned}$$
$$\begin{aligned} QoE_{HIF} = & \, 0.004+0.02\times MFCC_{2}+0.05\times sF0''+1.53\times Val \nonumber \\&-0.52\times Ar,\end{aligned}$$
$$\begin{aligned} QoE_{aBCI} = & \,0.08+0.86\times AI-0.23\times MBP-0.55\times MFCC_{2} \nonumber \\&+0.24\times sF0''. \end{aligned}$$

The obtained goodness of fit (\(r^2\)) value for model (6) was 0.76 with an RMSE of 0.136. For model (7), in turn, the obtained \(r^2\) value was 0.96 with an RMSE of 0.05, thus again highlighting the importance of HIFs in QoE perception modelling. Lastly, for model (8), the obtained \(r^2\) value was 0.87 with an RMSE of 0.117. When comparing the output of the objective QoE model in (8) and model in (7), a Pearson correlation coefficient of 0.91 was obtained.


In this section, we discuss the experimental results obtained from the subjective and objective methodologies used.

Role of HIFs in QoE modelling

Recently, HIFs and objective HIF characterization have gained burgeoning attention from QoE researchers [10, 59, 60]. Previously, researchers have investigated the effects of user expectation on QoE [61]. In the similar vein, this paper has evaluated the effects of users’ affective states on overall QoE perception. We have found evidence from the two subjective assessment tests (hands-free communication and TTS systems) that indeed the users’ perceived affective states change with varying speech quality. As is evident from the results, these changes were produced irrespective of the impairment type.

It is visible from the valence-arousal maps depicted by Figs. 7 and 9 that poor quality speech stimuli produced low arousal and low valence states, thus producing states ranging from ‘sad’ to miserable’ in listeners. High quality stimuli, on the other hand, incited high arousal and high valence states, thus making users feel ‘alert’ or ‘amused’. From Tables 2 and 3, it was also found that the measured HIFs showed high (significant) correlation with QoE-MOS. When HIFs were combined with existing state-of-the-art technology-centric speech quality metrics, e.g., as in (4) and (7), improvements in QoE measurement performance were observed and relative gains of 20.8 and 26.3 % were seen for far-field and TTS systems, respectively. These findings suggest that the affective states can indeed directly influence a listener’s perceived experience (or QoE) with a new telecommunication service.

Nonetheless, despite the improvements seen when adding HIFs to objective quality models [i.e., (4) and (7)], there was still a gap to perfect goodness-of-fit, thus suggesting that the inclusion of alternate additional HIFs may be important. To this end, future studies should investigate the effects of e.g., attention, cognitive load, fatigue and/or user engagement.

aBCI advantages and limitations

The use of affective BCIs during subjective QoE assessment has two major advantages. First, aBCIs may allow for monitoring of the listener’s affective states in an objective manner, thus potentially reducing listener biases in subjective tests, particularly for TTS systems [62]. To this end, typical EEG-based metrics were used to quantify two emotional primitives: arousal and valence. More specifically, the alpha-band frontal inter-hemispheric asymmetry index (AI) was used as a correlate of valence and the medial beta power (MBP) as a correlate of arousal [47, 48]. The gaps observed between models (4) and (5) for hands-free communications and between models (7) and (8) for TTS systems, however, suggest that improved EEG features may still be needed.

In order to better understand the observed gap between QoE models found with subjective and with aBCI features, Pearson correlations were calculated between AI and MBP and the subjective valence and arousal ratings. For dataset 1 (hands-free), it was found that AI was significantly correlated with valence with a correlation coefficient of 0.41 (\(p\le 0.05\)) and MBP was weakly correlated with arousal with a coefficient of \(-\)0.24 (\(p\le 0.1\)). For Dataset 2 (TTS), in turn, AI showed a significant positive correlation with valence (\(0.52; p\le 0.05\)) and MBP a weakly-significant correlation with arousal (\(-0.29; p\le 0.06\)). Overall, it is expected that more powerful models can be obtained once improved aBCI features are developed. Alternately, additional neuro-physiological signal modalities may be incorporated for human affective state monitoring, such as fNIRS, galvanic skin response, and eye tracking. The development of such “hybrid” affective BCIs is the aim of our ongoing research.

The second main advantage of using aBCIs to objectively monitor listener affective states is that it allows for continuous real-time monitoring of listener affective states. In practice, it is not possible to have listeners attend to the quality of a presented stimuli continuously, as well as report the elicited affective states. Such cognitive load demands will result in unwanted effects in the obtained ratings, as recently reported by [63]. As such, the use of an aBCI can allow the participants to focus on the QoE experiment fully, particularly if it involves time-varying distortions, such as voice over internet protocol (VoIP). While the present experiments did not involve time-varying distortions, the high correlations obtained between the objective and subjective ratings suggest that the proposed objective regressors could be used for such tasks. Overall, gains of 12.5 and 14.5 % in QoE measurement could be seen once aBCI features were used, relative to using only technological factors, for the hands-free and TTS systems, respectively.


Speech QoE perception is known to be influenced by internal human factors, as well as external technological and contextual factors. Existing objective QoE models, however, have focused mostly on the latter two and have omitted human QoE factors, such as affective states, from the equation. In this paper, we have taken the first steps towards showing the importance of incorporating human affective states into speech QoE models, both subjectively and objectively. Subjectively, we showed the impact of speech distortions on the listener’s perceived valence and arousal states, and in turn, their effect on perceived QoE. Objectively, on the other hand, we have proposed the use of affective BCIs to measure the listener’s valence and arousal levels. Through regression analysis, we showed that features extracted from an EEG-based BCI could improve QoE models performance by as much as 12.5 and 14.45\(\;\%\) for hands-free communication and TTS systems, respectively.