This article describes the first emotional corpus, named EMOVO, applicable to Italian language,. It is a database built from the voices of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral state. These emotions are the well-known Big Six found in most of the literature related to emotional speech. The recordings were made with professional equipment in the Fondazione Ugo Bordoni laboratories. The paper also describes a subjective validation test of the corpus, based on emotion-discrimination of two sentences carried out by two different groups of 24 listeners. The test was successful because it yielded an overall recognition accuracy of 80%. It is observed that emotions less easy to recognize are joy and disgust, whereas the most easy to detect are anger, sadness and the neutral state

Costantini, G

Iaderola, I

Paoloni, A

Todisco, M

Archivio della Ricerca - Università di Roma Tor vergata

EMOVO Corpus: an Italian Emotional Speech Database 
Giovanni Costantini1,2, Iacopo Iadarola3, Andrea Paoloni3, Massimiliano Todisco1,3 
1Department of Electronic Engineering, University of Rome “Tor Vergata”, Rome, Italy 
2Institute of Acoustics “O. M. Corbino”, Rome, Italy 
3Fondazione “Ugo Bordoni”, Rome, Italy 
E-mail: costantini@uniroma2.it, iacopo.iadarola@gmail.com, pao@fub.it, massimiliano.todisco@uniroma2.it  
Abstract 
This article describes the first emotional corpus, named EMOVO, applicable to Italian language,. It is a database built from the voices 
of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral 
state. These emotions are the well-known Big Six found in most of the literature related to emotional speech. The recordings were 
made with professional equipment in the Fondazione Ugo Bordoni laboratories. The paper also describes a subjective validation test of 
the corpus, based on emotion-discrimination of two sentences carried out by two different groups of 24 listeners. The test was 
successful because it yielded an overall recognition accuracy of 80%. It is observed that emotions less easy to recognize are joy and 
disgust, whereas the most easy to detect are anger, sadness and the neutral state. 
 
Keywords: emotional corpus, speech corpus, speech database 
 
1. Introduction 
Linguistic information is all that can be carried by text, 
but it is only a small part of the spoken message. As 
humans, when listening to speech, we are sensitive to 
extra-linguistic information about the identity and the 
state of the speaker, as well as to paralinguistic 
information about the speaker’s intentions underlying the 
utterance.  
An important role in the communication is given by the 
emotion (Guerrero et al., 1998). For example, a simple 
text dictation that doesn’t reveal any emotion, it does not 
covey adequately the semantics of the text. 
Speech emotion recognition (SER) systems can be used 
by actors for emotion speech consistency, by disabled 
people for communication as well as for interactive TV, 
for constructing virtual teachers, in the study of human 
brain malfunctions, and the advanced design of Text To 
Speech systems.  
To achieve such ambitious goals, the collection of 
emotional speech databases is a prerequisite. 
Studies on emotional speech have verified that there are 
strong correlates between speech and emotion.  
Major findings in this area are described in key review 
articles of (Davitz, 1964), (Scherer, 1986), (Murray and 
Arnott, 1993), (Cowie et al., 2001) and (Ramakrishnan, 
2012). The most commonly studied emotions in related 
studies are chosen from the “basic emotions” such as 
happiness, sadness, fear, disgust, anger, and surprise 
(Murray and Arnott, 1993), (Giovannella et al., 2009, 
2012). 
Different types of corpora have been used for the study of 
emotions in speech. Some groups have employed 
spontaneous emotional speech, trying to get the greatest 
authenticity in the emotions (Douglas-Cowie et al, 2003), 
(Cresti, 2005), (Burkhardt et al, 2005). Others have 
worked with induced emotions, putting the speaker into 
situations to rouse a specific emotion (Kuroda, 1976). A 
third option has been to use a speaker with acting skills to 
simulate emotions.  
Though this latter technique can exaggerate the emotions, 
the fact is that they are recognized, so that practical 
modelling can be derived from them. In this work we 
selected acted speech corpus because it is the only one 
that allows a complete control over the recorded text. 
2. Emotional speech database 
Prominent example of acted DB are the EMO (Berlin 
emotional speech), the DES (Danish Emotional speech 
corpus), Polzin in English and Groningen in Dutch. 
Induced corpora are Amir (Russian), Tolkmitt and Scherer 
in German Fernandez and al. in English. As concerning 
the real word corpus Recola in French, Belfast natural 
Database in English, Smartkom in German.  
A good list of emotional corpora is available in 
http://emotion-research.net/wiki/Databases. It is based on 
the Humaine deliverable D5c. 
EMOVO (Iadarola, 2009) is the first database of 
emotional speech for the Italian language. Six actors were 
summoned, three males and three females with proven 
expertise, and have made them perform fourteen 
sentences (assertive, interrogative, lists) based on six basic 
emotional states (disgust, fear, anger, joy, surprise, 
sadness) plus the neutral state. 
These emotions are the well-known big six (Cowie & 
Cornelius, 2003) found in much of the literature relating 
to emotional speech. The recordings were made, with 
appropriate professional tools, at the laboratories of the 
Fondazione Ugo Bordoni, 
In addition, EMOVO has been validated with a test of 
discrimination of emotions on two phrases: the house loud 
wants with the bread and the cat is flowing in pear 
conducted in parallel and separately.  
Both tests had a sample of 12 subjects recognizers, which 
had in turn indicate, choosing between two possible 
answers, the emotional state of the sentences heard.  
The test was successful because it was an overall accuracy 
of the recognition of 80%.  
We also assessed the scale of recognition of emotions, and 
there has been a substantial agreement with the scale of 
recognition inferred from the literature in this regard. 
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3. Choice of emotion and actors 
The emotional performance expressed in the database 
concern to the following: 
 
1. disgust 
2. joy 
3. fear 
4. anger 
5. surprise 
6. sadness 
7. neutral 
 
These are the emotions that have received more attention 
from the Italian scientific community and therefore it was 
decided to stay within this range of basic emotions rather 
than analyse others. 
To avoid too different interpretations of these emotions, 
we are given the appropriate explanation for the emotions 
with more problematic definition. In particular, we 
explained to each actor that anger to express is identified 
by Scherer Scherer K. R. (1979) as hot. While, for what 
concerns to disgust, we pointed out that it meant a 
physical rather than moral disgust. Regarding the surprise 
we invited the actors to maintain this emotional state for 
the entire duration of the sentence, and not to use for this 
purpose a questioning tone. For the rest of the emotions 
we have not considered it necessary to provide further 
explanation. Finally, we asked the actors not to deliver an 
explicit emotional indicators such as laughter, tears etc., 
which would distort the recognition test. 
We chose 6 professional actors, 3 males and 3 females, the 
age is from 23 to 30 years old and we care to check their 
professional background, especially their knowledge of 
the famous Stanislavsky method, consisting to self 
induced emotional states recalling the situations in their 
own life where he/she felt intensely the same emotion. 
The actors were classified as M1, M2, M3, F1, F2, F3. 
4. Linguistic material 
This is the list of phrases to elicit: 
 
1. Gli operai si alzano presto. 
2. I vigili sono muniti di pistola. 
3. La cascata fa molto rumore. 
4. L’autunno prossimo Tony partirà per la Spagna 
nella prima metà di ottobre. 
5. Ora prendo la felpa di là ed esco per fare una 
passeggiata. 
6. Un attimo dopo s’è incamminato ... ed è 
inciampato. 
7. Vorrei il numero telefonico del Signor Piatti. 
8. La casa forte vuole col pane. 
9. La forza trova il passo e l’aglio rosso. 
10. Il gatto sta scorrendo nella pera 
11. Insalata pastasciutta coscia d’agnello limoncello. 
12. Uno quarantatré dieci mille cinquantasette venti. 
13. Sabato sera cosa farà? 
14. Porti con te quella cosa? 
 
In English: 
 
1. Workers get up early.  
2. Firefighters are equipped with a gun.  
3. The waterfall makes a lot of noise.  
4. The next fall Tony will leave for Spain in the first 
half of October.  
5. Now I take the sweatshirt and go for a walk.  
6. A moment later he hath walked ... and stumbled.  
7. I would like the telephone number of Mr. Piatti. 
8. The strong house wants with bread.  
9. The force is up and red garlic.  
10. The cat is flowing in pear.  
11. Pasta salad leg of lamb limoncello.  
12. One forty-three ten thousand fifty-seven twenty.  
13. Saturday night, what will?  
14. Bring with you that thing?  
 
Regarding the content of the sentences the necessary 
condition is that the semantic value of this content be 
emotionally neutral in order to use this sentences also in 
subjective tests where the text meaning could influence 
the results. To this end it is possible to use phrases that do 
not relate specifically to a given emotional expression, 
and phrases "nonsense". If the former can pose a 
challenge to the actor to place them in the right emotional 
state, the latter involve the risk of being recited in a 
stereotypical manner, as the actor may not be able to 
"hear" as natural. Aware of these problems, we decided to 
use both categories (sentences 1-7, 13, 14 semantically 
neutral, sentences 8-10 nonsense), adding two sentences 
which are located in an intermediate position with respect  
 
The phrases are semantically neutral and are formed by 
short versions (1-3) and long (4-7). 
For the purpose of spectral analysis have been satisfied, 
the following basic conditions: 
 
• presence in the sentences of all the phonemes of 
the Italian language 
• presence in every sentence of a fair balance 
between voiced and unvoiced consonant 
5. Recording and storing data 
The performances of the actors were recorded in a room 
of the laboratories of the Fondazione Ugo Bordoni in 
Rome. Two professional microphones SHURE SM58LC 
model and a digital recorder Marantz PMD670 model 
were used. The recordings were performed with a 
sampling frequency of 48 kHz, 16 bit stereo, wav format.  
The actors were able to move freely and this has 
obviously affected the data on absolute signal intensity, 
depending on the distance of the mouth from the 
microphone. In other cases it is due to provide manual 
lowering of the volume of registration, in order to avoid 
the saturation of the signal in the cases of emotions that 
generate high levels of energy, such as anger. 
 
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 RECOGNIZED EMOTION 
NEUTRAL DISGUST JOY FEAR ANGER SURPRISE SADNESS 
E
L
IC
IT
E
D
 E
M
O
T
IO
N
 
NEUTRAL 93% 1% 0% 0% 4% 0% 2% 
DISGUST 3% 67% 2% 6% 10% 6% 6% 
JOY 2% 4% 65% 7% 7% 10% 4% 
FEAR 2% 7% 2% 74% 3% 3% 9% 
ANGER 1% 1% 1% 3% 92% 1% 1% 
SURPRISE 1% 3% 4% 1% 1% 81% 9% 
SADNESS 2% 2% 1% 3% 0% 0% 92% 
 
Table I: EMOVO validation test. 
 
 
Each recording session is about an hour. Subsequently 
recorded phrases were stored in the database in 6 different 
folders corresponding to the 6 actors. Each file has been 
labelled as follows: 
 
neu-m1-b1.wav 
 
The first part of the name indicates the emotional state 
(neutral, disgust, joy, fear, anger, surprise, sadness). 
The second part of the name indicates the actor/actress 
(m1, m2, m3, f1, f2, f3) that reads the sentence. 
The third part of the name indicates the type of sentence: 
 
• the sentences 1-3 are ranked respectively b1, b2, 
b3 (short) 
• the sentences 4-7 are ranked respectively l1, l2, 
l3, l4 (long) 
• the sentences 8-12 are ranked respectively n1, 
n2, n3, n4, n5 (nonsense) 
• the sentences 13,14 are ranked respectively d1, 
d2 (questions). 
 
Finally, all the sentences have been recorded into a 
database in Microsoft Access, in which we can sort the 
records according to the emotion, the text of the sentence, 
the sentence type, and we also can listen the sentence 
clicking on the proper link. 
In total 588 records have been archived: for each of the 6 
actors 98 sentences were recorded, corresponding to 14 
sentences spoken in 6 emotional states plus the neutral 
state. 
The total number of recorded material, excluding breaks, 
is approximately 10 minutes per actor, then an hour for 
the entire database. EMOVO is currently available on a 
single CD-ROM and will be distributed to a request. 
6. Applications of Emovo 
A corpus of emotional voice is essential to build systems 
of emotion recognition and systems of text to speech 
synthesis from text with emotional voice. The 
introduction of the appearance of emotion in human-
machine communication finds its most important 
application in man-machine interface and then in call 
canter, but also in many other areas. Among these we will 
quote the Lie detector (X13-VSA PRO) a system capable 
of judging what is he lies, the use in the automotive 
industry with important implications in the field of safety, 
the use in video games, in communication robot man, and 
in many other application fields such as e-learning. 
7. Validation test 
In order to evaluate the performances of emotional actors, 
or if the sentences produced contain the information 
needed to recognize the emotion that had been acted, we 
have built a subjective test of emotion recognition. After 
listening to a pre-test consisting of 6 sentences, one for 
each emotional state, subjects were asked to choose 
between two possible emotions (ex: anger / joy) then the 
listeners was subjected to full validation testing. For each 
actor were chosen 2 nonsense sentences to avoid that the 
semantic content could influence the choice between 6 for 
every emotion possible. The subject then had to choose 
between two possible emotions. A total of 42 items were 
proposed to subject choices (6 choices for 7 emotional 
states). 
Each subject listened the signals of a male and a female 
actor for a total of 84 tests. The accuracy was calculated 
according to the formula: 
 
Accuracy = (ca - wa) / nr 
 
where ca are the correct answers, wa are the wrong 
3503
answers and nr are the number of responses. 
The subjects were organized into two groups, both of 12 
elements, a group active in the laboratory that had 
acquired the corpus in Rome, and another in university 
laboratory in Cosenza. The results of the two laboratories 
are statistically compatible and are therefore summarized 
in a single table (see Table I) where the columns represent 
the emotion recognized and the lines that elicited. It is 
observed that the emotions less easily recognized are the 
joy and the disgust while the most easily recognized are 
the neutral, the anger and sadness. Validation test has been 
done to ensure the goodness of emotional performances of 
the actors, for this purpose we have built a subjective test 
of emotion recognition.  
Each listener heard a couple of sentences with different 
emotions, and then was asked to choose between two 
possible emotions such as disgust or joy. 
Listeners were organized into two groups, both of 12 
elements, a group at the laboratory that had acquired the 
corpus in Rome, and another in another laboratory in 
Cosenza. The results of the two laboratories are 
sufficiently compatible and are therefore summarized in a 
single table (see Table I), where the columns represent the 
emotion recognized and rows the one elicited. It is 
observed that emotions less recognized are the joy and 
disgust, while the overall accuracy of the recognition is 
about 80%. It is certainly true that good actor can generate 
speech that listeners classify reliably. 
8. Conclusion 
In the study of speech signal the emotional aspect plays an 
increasing role because only through emotional 
connotations become artificial voices more natural and 
because the emotion recognition is an important parameter 
in many applications. In order to characterize the 
emotional voices it is essential to have a reference corpus. 
Thus, the realization of EMOVO has made available to 
the scientific community the first corpus of emotional 
voice for Italian.  
This paper has presented a detailed description of the 
corpus and also some significant results of its subjective 
evaluation. Based on indications obtained in the tests, we 
believe that EMOVO is suitable for use in studies of 
emotion. However, a database which records sentences 
where emotion has been simulated by actors might be 
questioned for use in a system for automated recognition 
of emotions in speech. To override this potential problem, 
we aim at building an alternate corpus made up of 
sentences uttered by persons caught in a real emotional 
state 
The EMOVO corpus can be downloaded at:  
 
http://voice.fub.it/EMOVO 
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