Sumanta Karmakar

Pratik Dey

PhD Sahadev Roy

Speaker Recognition Sumanta Karmakar Pratik

Advanced Engineering

Speaker Recognition

Sumanta Karmakar and Pratik Dey

Introduction

The objectives speaker recognition is to get hands-on experience with signal processing in both the time and the frequency domain, to become familiar with MATLAB programming, and to receive a small bit of insight into the principles of speech analysis [START_REF] Quatieri | Discrete-time speech signal processing: principles and practice[END_REF]. This was accomplished by recording four speech segments from each person in the department all of them varying slightly. Comparisons and analysis were then made on each signal, dependent upon different condition [START_REF] Furui | Cepstral analysis technique for automatic speaker verification[END_REF]. These instructions involved six different procedures to apply to the recorded signals, which were speech editing, speech degradation, speech enhancement, pitch analysis, formant analysis, and waveform comparison [START_REF] Beek | An assessment of the technology of automatic speech recognition for military applications[END_REF]. Speech analysis was a simple cut-and-paste type procedure [START_REF] Chu | Speech coding algorithms: foundation and evolution of standardized coders[END_REF]. Speech degradation and speech enhancement were related sections, in which a signal was taken, noise was added, and then a low pass filter was used to help diminish that noise. Pitch analysis was a useful way to roughly tell if a speaker was male or female based on the average pitch derived from the pitch contour.

Progress of speaker recognition systems is based on characteristics of an individual's voice like a fingerprint reorganization system [START_REF] Strait | Musical training during early childhood enhances the neural encoding of speech in noise[END_REF]. Speaker identification study continues today under the area of the field of digital signal processing (DSP). In the current design project a basic speaker identification algorithm has been written to sort through a list of files and choose the 12 most likely matches based on the average pitch of the speech utterance as well as the location of the formants in the frequency domain representation [START_REF] Jayant | Digital coding of speech waveforms: PCM, DPCM, and DM quantizers[END_REF]. In addition, experience has been gained in basic filtering of high frequency noise signals with the use of a Butterworth filter as well as speech editing techniques.

Speech

Speech is the vocalized form of human language. It is depends upon the syntactic mixture of lexical and names that are drawn from very large [START_REF] Hollien | About forensic phonetics[END_REF]. Each verbal word is formed out of the phonetic permutation of a limited set of vowel and consonant units. These vocabularies, the syntax which structure set of language sound unit differ; create the existence of many thousands of different types of mutually unintelligible human languages [START_REF] Harper | Spoken language characterization[END_REF]. The vocal ability that enable human to create speech also provide humans with the ability to sing [START_REF] Akobson | The sound shape of language[END_REF]. Speech is researched in terms of the speech production and speech perception of the sounds used in vocal language.

Principle of Speaker Recognition

Speaker recognition is the process identification of a individual from uniqueness of voices which is also known as voice biometrics [START_REF] Campbell | Speaker recognition: A tutorial[END_REF]. There is a difference between speaker recognition (recognizing who is speaking) and speech recognition (recognizing what is being said) voice recognition can be used for both. Recognizing the speaker can make simpler the task of translating speech in system that have been taught on specific person's voices or it can be used to validate or verify the identity of the speaker [START_REF] Taylor | The communicational basis of organization: Between the conversation and the text[END_REF]. Speaker verification has earned speaker recognition its classification as a behavioral biometric. The system that we will describe is classified as text-independent speaker identification system since its task is to identify the person who speaks regardless of what is saying. Speaker recognition can be classified into identification and verification. Speaker identification is the processes of formative which register speaker provide a given utterance [START_REF] Tao | A review of contributions by Australian research institutions into speech processing[END_REF].

Analysis of Discrete-Time Speech Signals

Most speech processing applications utilize certain properties of speech signals in accomplishing their tasks. This section describes these properties or features and how to obtain them from a speech signal s(n),i.e., speech analysis. This typically requires a transformation of s(n) into a set of parameters, or more generally into a set of signals, with the purpose often being data reduction. The relevant information in speech can often be represented very efficiently [START_REF] Cotos | Automated writing evaluation for non-native speaker English academic writing: The case of IADE and its formative feedback[END_REF]. Speech analysis •Linear predictive speech analysis based on the characterization of all-pole digital filters;

•Cepstral analysis of speech signals based on a specific DSP technique for mixed linear and nonlinear discrete-time system analysis;

•Speech formant tracking based on analysis of digital resonant systems;

•Voicing pitch tracking based on time and frequency analysis techniques for discrete-time signals;

and

•Speech analysis using auditory models based on filter-bank signal decomposition.

All these analysis techniques have significant and wide applications in speech technology and in enhancing one understand of the fundamental properties of the speech process. Technical and Scientific Publisher www.tscipub.com

Time-Domain and Frequency-Domain Properties of Speech

Analyzing speech in the time domain often requires simple calculation and interpretation in frequency domain. Most models of speech production assume a noisy or periodic waveform exciting a vocal-tract filter [START_REF] Reynolds | Speaker verification using adapted Gaussian mixture models[END_REF]. The excitation and filter can be described in either the time or frequency domain, but they are often more consistently and easily handled spectrally. For example, repeated utterances of the same text by a single speaker often differ significantly temporally while being very similar spectrally. Human hearing seems to pay much more attention to spectral aspects of speech (especially power distribution in frequency) than to phase or timing aspects.

Waveforms

Time-domain speech signals are also called speech waveforms. They show the acoustic signals or sounds radiated as pressure variations from the lips while articulating linguistically meaningful information. The amplitude of the speech waveform varies with time in a complicated way, including variations in the global level or intensity of the sound [START_REF] Auckenthaler | Score normalization for textindependent speaker verification systems[END_REF]. The probability density function of waveform amplitudes, over a long-time average, can be measured on a scale of speech level expressed as sound dB. This function has a form close to a double-sided (symmetric) exponential at high amplitudes, and is close to Gaussian at low amplitudes. The entire probability density function can be approximated by a sum of exponential and Gaussian functions. Such distributions can be exploited in speech coding and recognition.

Fundamental Frequency

Under detailed examination, a speech waveform can be typically divided into two categories:

1. a quasi-periodic part which tends to be repetitive over a brief time interval;

2. a noise-like part which is of random shape.

For the quasi-periodic portion of the speech waveform, the average period is called a fundamental period or pitch period. Its inverse is called the fundamental frequency or pitch frequency, and is abbreviated F0. (Although pitch is actually a perceptual phenomenon, and what is being measured is actually F0, we follow tradition here and consider pitch synonymous with F0.) The fundamental frequency corresponds to vocal cord vibrations for vocalic sounds of speech. F0 in a natural speech waveform usually varies slowly with time. It can be 80 Hz or lower for male adults and above 300

Conclusion

Overall, the project went very well, and we have learned quite a lot. Speech editing is nothing more than moving about some arrays of numbers. Enhancement filters can be used to remove both natural and intentional noise, to a reasonable extent. And pitch and formant analysis can be used to give a general idea of whether two speakers are the same person or not. There are also other factors, beyond speaker variability, that present a challenge to speaker recognition technology. Examples of these are acoustical noise and variations in recording environments (e.g. speaker uses different telephone handsets).The defect, however, are obvious in the waveform comparison. While these approaches can be used to give a rough estimate or to aid in human decisions about whether two voices are the same, computer programs like these are simply not advanced enough to be completely automated and foolproof. In other words, this is not a black box where you do not have to know anything about how the program works and just expect an accurate answer based on a certain set of inputs. Other things that we would like to explore in the subject include Delta-Cepstrum coefficients and perceptual linear predictive coefficients in order to see how much they could help with or replace pitch and formant analysis. Maybe a combination of all four would give a much higher confirmation percentage.

Fig 1 .

 1 Fig 1. Basic structures of speaker recognition systems (a) Speaker identification (b) Speaker Verification

  are pertinent for different applications, while removing irrelevant aspects of the speech. Several important speech analysis techniques include:•Time-frequency representation of speech signals based on a STFT analysis;

Fig 2 .

 2 Fig 2. DTFT waveform

Hz for children and some female adults. F0 is the main acoustic cue for intonation and stress in speech, and is crucial in tone languages for phoneme identification. Many low-bit-rate voice coders require F0 estimation to reconstruct speech.

Overall Power

The overall power of the speech signal corresponds to the effective sound level of the speech waveform averaged over a long-time interval. In a quiet environment, the average power of male and female speech waveforms measured at 1 cm in front of a speaker's lips is about 58 dB. Male speech is on average about 4.5 dB louder (greater power) than female speech. Under noisy conditions, one's speech power tends to be greater than in a quiet environment (i.e., we speak more loudly in order to be heard). Further, not only the overall power and amplitude is increased, but also the details of the waveform changes in a complicated way. In noisy environments a speaker tends to exaggerate articulation in order to enhance the listener's understanding, thereby changing the spectrum and associated waveform of the speech signal.

Overall Frequency Spectrum

While the spectral contents of speech change over time, if we take the discrete-time Fourier transform (DFT) of the speech waveform over a long-time interval, we can estimate the overall frequency range that covers the principal portion of the speech power. Such information is important for the design of speech transmission systems since the bandwidth of the systems depends on the overall speech spectrum rather than on the instantaneous speech spectrum. When such an overall frequency spectrum of speech is measured in a quiet environment, it is found that the speech power is concentrated mainly at low frequencies. For example, over 80% of speech power lies below 1 kHz. Beyond 1 kHz, the overall frequency spectrum decays at a rate of about -12 dB per octave. Above 8 kHz, the speech power is negligible. In a telephone channel, due to telephone bandwidth limitations, the overall frequency spectrum becomes negligible above 3.2 kHz, losing some information, mainly for consonants. If the long-time Fourier transform analyzes only a quasiperiodic portion of the speech waveform, we will see the frequency components in harmonic relations, i.e., integer multiples of a common frequency. This common frequency, also called the lowest harmonic component, is the pitch.


Irina Toma
email: irina_toma@rocketmail.com

Cristina-Elena Alexandru

Mihai Dascalu
email: mihai.dascalu@cs.pub.ro

Philippe Dessus
email: philippe.dessus@univ-grenoble-alpes.fr

Stefan Trausan-Matu
email: stefan.trausan@cs.pub.ro

Semantic Taboo -A Serious Game for Vocabulary Acquisition

Keywords: serious games, vocabulary acquisition, word-guessing, Taboo game, ReaderBench framework

Vocabulary acquisition is considered one of the most important aspects of a language because it defines and constraints communication. In order to overcome this, students and teachers use various techniques meant to improve the learning process and facilitate word acquisition. Using conceptual networks that establish links to prior knowledge is an approach with proven good results. The game presented in this paper, Semantic Taboo, is a serious game that helps users enlarge their conceptual network. The game has two modes, one for teachers and one for students. In the teacher module, users have the option of adding playing cards. The theme of the card (the seed) is taken as input, while the list of taboo words is selected from the list of semantically related words provided by the ReaderBench framework. The student model has the same game principle as the traditional game, but the list of taboo words and the description of the seed help the learner acquire new words. The preliminary validation consisted of 15 users who were impressed by the game concept and its educational value.

Introduction

The term of serious games is being used to refer to games that have a positive impact on users' skills. This definition includes not only computer games or games that focus on learning, but also those used for medical treatments, marketing tools, board games, etc. Since serious games reflect such a broad concept, this paper focuses on vocabulary games which help users learn and exercise words in a foreign language.

Learning a new language (L2) is a difficult task for both the learner and his/her teacher because of the large amount of words to be assimilated. Learning strategies are usually divided into two categories [START_REF] Wu | Designing a smartphone app to teach English (L2) vocabulary[END_REF]: incidental learning and intentional learning. Intentional learning consists of memorizing words and repeating them until they are familiar. To make this task more interactive, teachers create word lists or cards and link words with their translation in a known language (L1) [START_REF] Wu | A Rote Strategy in Memorizing Vocabulary for ESL Learners[END_REF]. Proficient learners even apply different learning techniques to create links between concepts and previous knowledge, such as inferring word meaning from the given context. Incidental learning consists of performing tasks that are not strictly focused on learning, but have learning as a side-effect. This can be best exemplified by communicating and reading [START_REF] Laufer | Incidental vocabulary acquisition in a second language: The construct of task-induced involvement[END_REF].

No matter the used method, learners assimilate vocabulary as discrete entities (individual words) or as lexical phrases that are associated pragmatically with other known words. Considering this, a person's lexicon can be assessed as the number of words learned and the connections between the words [START_REF] Ellis | Understanding Second Language Acquisition 2nd Edition-Oxford Applied Linguistics[END_REF]. The goal of the game described in this paper is to improve the connections between words from the user's vocabulary.

The paper continues with a state of the art on vocabulary acquisition techniques, followed in the third section by a description of conceptual networks. Section four introduces several examples of vocabulary games that follow the previously presented concepts. The next two sections are reserved for the ReaderBench framework, as well as the Semantic Taboo game description and flow. Results, discussions and conclusions are presented in the last two sections.

Vocabulary acquisition techniques

Vocabulary is a fundamental element of all languages, without which learners would be inherently limited when expressing themselves. Having this in mind, the difficulty when learning a new language is acquiring a sufficient number of vocabulary words to become fluent in that language [START_REF] Kim | The role of task-induced involvement and learner proficiency in L2 vocabulary acquisition[END_REF]. Below we present the most frequently used techniques by students and teachers to make the acquisition process easier and more rewarding.

Extensive reading

Reading is part of incidental learning through which users not only enhance their vocabulary, but also improve their spelling and overall language skills. Extensive reading is a pleasurable reading action in which students are encouraged to choose reading materials from the ones recommended by their teacher. The amount of words learned varies considerably based on the frequency of the target words in the text. Moreover, students are more likely to remember a word if they encounter it multiple times [START_REF] Pigada | Vocabulary acquisition from extensive reading: A case study[END_REF].

Another influencer for extensive reading is the target words. These can be partially known, and in this case users understand better how the words are used in different contexts, or unknown. In the latter case, learners either guess the word from the context, or search its meaning in the dictionary. [START_REF] Gu | Vocabulary learning in a second language: Person, task, context and strategies[END_REF] argued that the dictionary method has better results and that students learn more words and remember them for a longer period of time.

Extensive Listening

Extensive listening focuses on early learners and consists on teachers reading different materials. For a better understanding and vocabulary acquisition, teachers can explain word meanings as they pass though the text [START_REF] Brown | Incidental vocabulary acquisition from reading, reading-while-listening, and listening to stories[END_REF]. By combining this technique with extensive reading, students read-while-listen, meaning that the teacher reads the text and the students follow the same text. The reading material can include images, to help the user picture the context. This brings advantages compared to only using one of the previously described techniques. One advantage is that learners can pay attention to the rhythm of the language and understand the segmentation and the coherence of sentences. When reading alone, students can miss the meaning of group of words and try to understand them as separate entities. While the teacher reads, learners comprehend the meaning of the words from their context.

Word-Focused Tasks

Word-focused tasks promote vocabulary learning through exercises like information gap, sentence writing and word definition [START_REF] Laufer | Reading, Word-Focused Activities and Incidental Vocabulary Acquisition in a Second Language[END_REF]. These exercises first expose the user to the target words in different reading materials and afterwards exercise the words. This technique offers a greater advantage compared to extensive reading or listening because students can use the words productively in a conversation or in written texts [START_REF] Peters | Learning L2 German vocabulary through reading: The effect of three enhancement techniques compared[END_REF].

Other word-focused activities are list of words and flashcards [START_REF] Uriarte | Vocabulary Teaching: Focused Tasks for Enhancing Acquisition in EFL Contexts[END_REF]. Lists of words are used as a mapping between known words and target words and flashcards have a visual representation, associated pronunciation and spelling. Using these materials, students create a contextual mapping [START_REF] Wolter | Lexical network structures and L2 vocabulary acquisition: The role of L1 lexical/conceptual knowledge[END_REF] for the new words that help them integrate the L1 or L2 conceptual network described in detail in the following section. We must emphasize from the beginning the difference between word learning and concept learning: a person can learn words without necessarily understanding them conceptually. Thus, our aim is to support in-depth concept comprehension by defining semantically cohesive contexts. In addition, we must also take into account prior knowledge from student's mind [START_REF] Bereiter | Education and mind in the knowledge age[END_REF], therefore greatly impacting the conceptualization of new word acquisitions.

Storing vocabulary knowledge in conceptual networks

A conceptual network is a graph with connections between words. Two words are connected if they represent similar concepts [START_REF] Motter | Topology of the conceptual network of language[END_REF]. When learning a new word, usually from a written text, students associate it with existing knowledge, therefore expanding their conceptual network. The learning process is affected by the nature of the word, the text's difficulty, the existing links from the student's contextual network and his/her motivation to learn [START_REF] Nassaji | The relationship between depth of vocabulary knowledge and L2 learners' lexical inferencing strategy use and success[END_REF]. At the beginning of the learning process, students have a nearly empty L2 conceptual network and they use the initial L1 network as a placeholder [START_REF] Wolter | Lexical network structures and L2 vocabulary acquisition: The role of L1 lexical/conceptual knowledge[END_REF]. This is sufficient in most cases because words usually reflect universal properties. Still, some words require a lexical restructuring of the conceptual network because the concepts from L1 no longer match with those from L2. [START_REF] Wolter | Lexical network structures and L2 vocabulary acquisition: The role of L1 lexical/conceptual knowledge[END_REF] presents an example of the collocation "furry dog" that is acceptable in English, but "fuzzy dog" or "frizzy dog" are less likely to be encountered, even though the three adjectives are semantically similar.

Contextual networks are defined by breadth and depth. [START_REF] Read | Plumbing the depths: How should the construct of vocabulary knowledge be defined[END_REF] gives the following definitions: breadth is the number of words a person has in his/her vocabulary, while depth represents the quality of the knowledge. Quality does not refer only to the different meanings of the word, but also to pronunciation, spelling, syntactic and semantic relations with other words [START_REF] Nassaji | The relationship between depth of vocabulary knowledge and L2 learners' lexical inferencing strategy use and success[END_REF].

Both breadth and depth knowledge can be assessed through vocabulary tasks. Since breadth refers strictly to the quantitative aspect of the vocabulary, the tools used are checklists where students mark the word known or unknown, word translations into L1 or synonym identification in a multichoice test. The evaluation of knowledge depth is more problematic. [START_REF] Read | Assessing vocabulary[END_REF] presents a test called Word-Associate Test (WAT) meant to measure the depth of vocabulary by evaluating the semantic and collocational relations between words.

Vocabulary games

From the description of the breadth of evaluation tools, it is understandable that each task can be integrated into one or several games. Games help students learn the target word in a fun and competitive environment and keep them motivated to continue the learning process. On the other side, teachers can revise the vocabulary and grammar in a shorter timeframe and can benefit from potential automated evaluation methods.

Since there is a considerable amount of evaluation tools, vocabulary games have been divided into categories based on the type of actions performed by students [START_REF] Tuan | Vocabulary recollection through games[END_REF]:

• Information gap -students have to fill-in sentences with missing information; • Guessing games -players have to guess information withheld by other users; • Search games -similar to the information gap games, but the amount of information is larger; • Matching games -students match information with various concepts, pictures or cards. • Role-play games -students play different roles to simulate real life situations and to exercise specific vocabulary.

Existing games

Vocabulary acquisition is one of the most systematic activities when learning a new language in school contexts; thus, teachers try to make the lessons more interactive and more motivating for the students. This is done by introducing different activities, such as flash cards, word lists or concept maps. Despite this, new concepts should be personalized for each individual student due to different knowledge backgrounds. The next section presents serious games that support users in their vocabulary expansion and are also adaptable to each user's need.

Betty's Brain

Betty [START_REF] Wagster | Bringing CBLEs into classrooms: Experiences with the Betty's Brain system[END_REF]) is a virtual teaching agent whose purpose is to help students research and build models of science phenomena available in the real world. The idea of the game is that Betty answers questions from a virtual mentor and students must analyze her answers and correct her. Betty responds according to a conceptual map and, if questioned, she can provide explanations for her answers which help students identify the texts that were used to generate the responses.

Students are assigned reading materials, which are translated in the conceptual map that Betty uses. This way, the game promotes a shared responsibility as students teach Betty and she answers questions. The conceptual map, that has also a visual representation, organizes the scientific content and helps students understand Betty's reasoning. The game as a whole is motivating for the users because they see their work applied successfully on another entity.

Taboo related games

Taboo is a word-guessing multiplayer game in which one player, the teller, describes the word from a card, while the others try to guess it. The card displays a list of forbidden words that the teller cannot use, thus fostering his or her word knowledge through reformulation and rephrasing; otherwise, the player is disqualified.

This game can be easily adapted and provided in an educational context. Without changing anything in the game, new game cards can be added to exploit to different scientific domains. [START_REF] Capps | Chemistry taboo: An active learning game for the general chemistry classroom[END_REF] and Olimpo, Davis, Lagman, Parekh, and Shields (2010) reflect this purpose-shift [START_REF] Djaouti | Classifying serious games: the G/P/S model. Handbook of research on improving learning and motivation through educational games[END_REF]) by using the game for teaching chemistry and biology. This way, students review their knowledge in an entertaining and engaging way, while concurrently promoting a better understanding of the meaning of the terms.

Von [START_REF] Ahn | Labeling images with a computer game[END_REF] expanded the idea of the card-game Taboo to a serious game that labels the images available on the internet. This game, called ESP Game, is an online game played by two users. They both see the same image and label what they see. Once the two players write the same label, they receive a new image and the word they agreed on becomes a label for the image. The resemblance with Taboo is that it uses a list of forbidden words that players cannot use as guesses.

Starting from the ESP Game, the same authors proposed a new version of Taboo, called Verbosity (Von [START_REF] Ahn | Verbosity: a game for collecting common-sense facts[END_REF]. The purpose of the game consisted in constructing a database of "common-sense facts". This seems just a side effect from a player's perspective because the gameplay is centered on word guessing. Verbosity is an online two-player game where one player is the Narrator while the other is the Guesser. The Narrator has a secret word and a list of hints structured as templates. The Narrator fills in the templates with any words, the secret word excepted. The Guesser types any word that (s)he thinks it matches the description and the Narrator tells her/him if it's close to the secret word. The template that the Narrator fills in represents the common-sense fact to be associated with the given word.

The ReaderBench framework

ReaderBench [START_REF] Dascalu | Analyzing discourse and text complexity for learning and collaborating[END_REF][START_REF] Dascalu | Mining texts, learner productions and strategies with ReaderBench[END_REF][START_REF] Dascalu | ReaderBench: The Learning Companion[END_REF] is an advanced Natural Language Processing framework that focuses on text cohesion. It was designed as an educational tool to enhance the learning process by assisting both teachers and students. On one hand, teachers can better evaluate the complexity of the textual materials provided to their students, the level of interaction and collaboration between different groups and the students' responses to the textual tasks (such as summaries and self-explanations). On the other hand, students can identify their level of comprehension and evaluate their learning methods and skills.

The framework exposes a REST API through which its main functionalities can be exploited. From these features, one is of particular interest for our Semantic Taboo serious game: the service that identifies semantically similar words. This service takes as input [START_REF] Gutu | ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes[END_REF]: a) the target word, b) the used language (i.e., English or French), c) the methods used to calculate the semantic similarity between words (such as semantic distance in lexical databases -WordNet and WOLF (Wordnet Libre du Français), Latent Semantic Analysis, Latent Dirichlet Allocation or word2vec, and d) the corresponding corpus (e.g., TASA or "Le Monde"; http://lsa.colorado.edu/spaces.html). The output of the service is a list of semantically similar words and their similarity score calculated using the corresponding semantic model. The game presented in this paper uses English language as parameter, LSA as the semantic model, and TASA as the corpus.

Semantic Taboo

Semantic Taboo is a serious game that follows the same concept as the traditional game, but its purpose is to focus on key words that users must learn or repeat. The target and taboo words are displayed on virtual cards that are either created by teachers or are generated randomly using ReaderBench. When the game starts, one user is assigned the host role and receives the game card, while the other players have to guess the taboo word.

User Interface

Semantic Taboo has an attractive and user-friendly web interface (see Figure 1) developed in JavaScript in which are displayed the four types of interaction the game provides: "How to play", "Create game", "Join game" and "Add a new card", as illustrated in Figure 1. Each game consists of one host and one or more guessers. When selecting "Create game", the user is assigned the host role, whereas "Join game" assigns the user the guesser role. The host role is to describe the target word without using any of the taboo words and the guessers must find the target word from the description. In order to make this communication easier, the game has a simple chat integrated that is always available to the users.

The guesser interface is straightforward, consisting of only an input field to send word guesses and the chat window in which users can talk. Each time a guesser introduces an answer in the input field, the word is verified against the target word; if it is a match, the user is awarded a number of points equal to the length of the target word. If the word is incorrect, the user is penalized by one point in order to discourage the input of random words and to encourage thinking. Once the target word is guessed, all the users are notified in the chat window and the game moves to the next round.

The host interface is more complex, containing the scores in the top part of the screen, the taboo card in the left part and the chat window in the right part (see Figure 2). The host must describe the target word on the card without using any of the taboo words. Each message introduced in the chat by the host is verified against the taboo words. Another interaction with the game consists of adding a new card (see Figure 3). Cards can be created by teachers or can be generated randomly. Teachers can create cards in a designated game interface that allows them to input one target word and 12 taboo words. This information is stored in the game's database and can be used later in the follow-up games. The second way of creating cards is to automatically generate them using ReaderBench. This method uses a dictionary where the entries consist of a lemma and lexemes, the inflectional forms. The taboo word is chosen randomly from the inflectional forms, with the limitation that the length of the word should be more than 7 characters in order to ensure a minimum game complexity. Afterwards, the list of 12 words is populated using the web service provided by ReaderBench that generates semantically similar words. Words are filtered based on their similarity score and the first 12 results are chosen as taboo words. If ReaderBench returns less than 12 words, the current target word is considered too complex, therefore it is dropped and the whole generation algorithm is reiterated. The overall flow within the game is depicted in Figure 4.

Educational scenarios

The educational scenarios in Semantic Taboo are available in both the host and the guesser profiles. The taboo words from the host's interface are similar to the target word, therefore the user can enhance his/her vocabulary with the available words. Also, when describing the target word, the host scans his/her conceptual network to find related concepts to the target word.

On the other side, guessers are presented a definition of a term previously learned, so they investigate their vocabulary to find the target word. Even though they do not find the target word, all the guesses help them enhance their conceptual network and make new connections.

Results and discussions

A group of 15 students (10 males and 5 females) aged 20-25 were asked to play the game and answer a 13 questions survey. The survey was divided in 10 questions with ratings on a 5-point Likert Scale (1 -completely disagree; 5 -completely agree) covering the gameplay of Semantic Taboo (presented in Table 1) and another 3 free answer questions (presented in Table 2).

Based on the participants' ratings, 92% of the users enjoyed playing the game and 98% considered that the game helped them improve their vocabulary. Moreover, more than half the users believed that the target word was easy to find despite the 12 taboo words. As a side effect, users thought the chat encouraged communication between them, improving their vocabulary and their writing skills. The Add new card functionality was received with less enthusiasm than we expected as users considered that it would be easier to suggest words for teachers to be added in taboo list. From the free answer questions, users emphasized the competitive aspect of the game and seemed satisfied with the chat functionality. They also enjoyed the colorful interface and the simplicity of the game. On the other hand, while playing the game with random cards, half the users came across unknown words and suggested adding definitions for unknown words. Considering the feedback, the first item to address consists of improving the Add new card functionality so that words are suggested to teachers. As teachers input the target word, the application will trigger a call to ReaderBench to provide similar concepts. Teachers will have the option to choose words from the similar concept list, or input their own. Also, to double check the taboo words, automated calls will be made to calculate the similarity between the target word and each taboo word. Words with a low similarity score will be highlighted in the interface.

A second improvement is to create an online notebook for each user, host or guesser, where a user can input words definitions and create concept maps to better reflect the current context. Moreover, a dictionary functionality will be added for both for the host and the guessers in order to extend even further the benefits of the online notebook. For the host, each of the taboo words will display a tooltip with the definition of the word and possible usages. The host will have the option to save the definitions to their online notebook. On the other hand, the guesser will be able to see the definition for each inputted guess of the players and for the target word, once this is found.

One last improvement to implement is the option to display the taboo words to the guessers at the end of the game in order to study other similar concepts. This feature will be enabled by default for the randomly generated cards and will be optional for the manually created cards.

Conclusion

Summing up, Semantic Taboo follows the footsteps of the initial game, but brings educational value by providing the list of taboo words with semantically similar concepts. This way, students improve their vocabulary and enhance their conceptual network with new terms and relations.

The game also provides a method to create new cards, either by generating them automatically using ReaderBench framework, or by inputting them manually. An enhancement for the manual method is to suggest the taboo words and double check the inputted words similarity coefficient to the target word.

Based on the feedback collected from the users, the game has an enjoyable interface which is both easy to play and competitive. The chat option has a beneficial side effect; even though it was created just for displaying messages, students use it in order to communicate and share opinions, therefore improving their vocabulary and writing skills.

Figure 1 .

 1 Figure 1. Starting screen of Semantic Taboo

Figure 2 .

 2 Figure 2. Host interface of Semantic Taboo

Figure 3 .

 3 Figure 3. Adding a new card in Semantic Taboo

Figure 4 .

 4 Figure 4. Activity diagram for Semantic Taboo

Table 1 .

 1 Semantic Taboo survey

	No.	Question

Table 2 .

 2 Semantic Taboo free answer questions

	No.	Question
	1	What did you like?
	2	What would you improve?
	3	What can help you improve your skills while playing this game?

Acknowledgments

This work has been partially funded by the EC H2020 project RAGE (Realising and Applied Gaming Eco-System) http://www.rageproject.eu/ Grant agreement No 644187, as well as by University Politehnica of Bucharest through the "Excellence Research Grants" Program UPB-GEX 12/26.09.2016.