Raed Belhassen
email: brmusique80@yahoo.fr

The notion of Ethos in Arabic music: computational modeling of Al-Urmawi's modes (13th Century) in Csound

The notion of ethos in Arabic music is outlined in this paper through the writings and thoughts of al-Kindi, Ziriab, Ikhwan al-Safa, Avicenna and Saffiyu al-Din al-Urmawi. The approach developed by al-Urmawi in his book "The book of cycles" will be studied and the ties woven between 13th Century modes and their ethos will be underlined. A computational model of these modes will be proposed using an object library developed in a Csound environment. A computational model of Saffiyu al-Din al-Urmawi's intervallic system will be performed and the twelve cycles he defined in his book will be classified according to their ethos. The modeling of some of the idioms pertaining to Arabic music, such as the homophonic aspect of musical rendition, or ornamentation, will be addressed. Intrinsic units of the object library: UDO (User Defined Opcodes), function tables, Opcodes (operation codes), loops and conditions will be explained. Finally, the possibility of studying the effect these modes create on people will be considered.

Introduction

The notion of ethos in Arabic music has always been ever-present. Terms in ancient writings such as "ta'thir" (influence), or "hal" (state) refer to the relation between ethos and music. In the 13th Century, Saffiyu al-Din al-Urmawi "was the first to make an attempt at drawing up the ethos of the modes" [START_REF] Poché | Le tronc commun des maqâms[END_REF]. In order to study this influence of music and modes on the individual following the thoughts of al-Urmawi, it is essential to reproduce the modes described in his book while respecting some of the idioms of Arabic music of his time. First, this paper will propose details on the notion of ethos through the writings and thoughts of Arabic theorists ranging from the 9th to the 11th Century. It will especially highlight the classification of modes according to their ethos in al-Urmawi's book in the 13th Century. Then, within the framework of the creation of an object library in Csound, this paper will propose a computational model of the twelve modes described by al-Urmawi, following a model representative of its non-tempered musical scale.

It will then try to model some of the idioms in Arabic music such as its homophonic nature or Arabic techniques of musical rendition based on ornamentation. Finally, this paper will explain how this object library works.

The notion of ethos in Arabic musical spirit

The notion of ethos was ever-present in the writings and thoughts of Arabic theorists. On this matter, Philip D Schuyler wrote [START_REF] Schuyler | Moroccan Andalusian Music[END_REF]: "Middle Eastern theorists, like the Greeks before them, recognized that each mode had a certain ethos1 ". Farmer [START_REF] Farmer | A history of Arabian Music[END_REF] noted: "The doctrine of the ethos (ta'thir) was now definitely linked up with music. This old Semitic idea had been strengthened by the doctrines of the Sabi'a of Harran and the theories of the ancient Greeks and Byzantines." C. Poché [START_REF] Poché | Le tronc commun des maqâms[END_REF] defined ethos as "the relation between musical and extramusical". Then he made the connection with maqâm: "that is to say the relation between maqâm, the individual, the world surrounding him, and the cosmos."

The Arabic Maqâm must first be defined.

Maqâm

The word maqâm means mode in English and "place" in Arabic. It started to mean, in the 18th Century, the location of an area on the handle of a musical instrument and describes a series of intervals grouped by genre or by family (jins) [START_REF] Poché | Le tronc commun des maqâms[END_REF]. The word jins, or set in English, describes a distribution of melodic cells, intrinsic to each maqâm, in trichord, tetrachord and pentachord.

Below is an instance of maqâm called Bayati, with its endogenous melodic cells and its variants. The jins Bayati in D is a fundamental cell used as a returning point for each modal exploration; when the second cell is a Hijaz, the maqâm changes aspect and is then called Bayati Churi (see Figure 1).

2.2

Historical approach of ethos in Arabic music:

2.2.1
The notion of ethos in Arabic musical thought in the 9th and 10th Centuries

According to Farmer [START_REF] Farmer | A history of Arabian Music[END_REF], Al-Kindi (Arabic philosopher, 801-873) linked music and astrology: "Almost everything terrestrial was influenced by something celestial.

The seven notes of the scale corresponded to the planets. The twelve signs of the zodiac were associated with the four pegs, four frets, and four strings of the 'ud. The four strings were affiliated with the primeval elements, the winds, the seasons, the humours, the mental faculties, colours, perfumes, the quarters of the zodiac, moon, and the world. Al-Kindi deals with this question at considerable length."

In the same period, Ziriab2 "went further, and developed a system of twenty-four modes -one for every hour of the day, each with inherent temporal, seasonal, and emotional characteristics" [START_REF] Schuyler | Moroccan Andalusian Music[END_REF].

In the 10th century, Ikhwan al-Safa (the brethren of purity3 ) said: "It is clear that to the movement of the spheres and stars are notes (naghamat) and melodies (alhan) [START_REF] Farmer | A history of Arabian Music[END_REF]. Just like in al-Farabi4 's writings, the word "ta'thir" (ethos) can often be found in the writings of Ikhwan al-Safa: "Although he was strongly influenced by Greek thinking, al-Farabi ventured to comment on the pleasure and lack of pleasure provoked by this or that interval type, whether consonant or dissonant. It is already considering that the world of sound may have an impact on the one who perceives it" [START_REF] Poché | Le tronc commun des maqâms[END_REF].

The notion of ethos in Arabic musical thought in the 11th Century

Avicenna (Persian philosopher and scientist, 980-1037) is one of the first Arabic theorists who took a radical stand on this issue: "finding a relation between the state of the sky, the states of the soul and musical intervals is no longer an issue" [START_REF] Erlanger | La musique arabe. Librairie orientaliste Paul Geuthner[END_REF], "the study of musical theory, therefore its component, was restricted to physical notions and as such one had to move away from the relation existing between the sound and the individual who was perceiving it" [START_REF] Poché | Le tronc commun des maqâms[END_REF].

In the 13th Century, the notion of ethos returned with Saffiyu al-Din al-Urmawi.

The notion of ethos in Arabic musical thought in the 13th Century:

A distinct relation between the mode and its emotional nature appear in al-Urmawi5 's writings. In his book "Kitab al-adwar" (The book of cycles) he defined twelve modes 6 and listed their ethos in a precise manner: -'ussaq, nawa et busalik : Character of strength, courage and bravery7 . -Rast, nuruz, 'iraq et isfahan: peaceful character and peace of mind. -Bozorg, rahawi, zirafkand, zangualah and husayni: sad character.

Poché [START_REF] Poché | Le tronc commun des maqâms[END_REF] pointed regarding the Hijaz mode, the relation of which to ethos in particular was not established by al-Urmawi in his book, characterized by its interval in augmented second: "Regarding augmented second: oral tradition in Turkey as well as Greece and in the Arabic world teaches us that this interval favors a better sleep and this is the reason why it can be found in many lullabies in that geographical areas."

The modes described by Saffiyu al-Din are based on an intervallic system of the time which was the subject of several chapters in his book.

Musical and computational modeling: an object library in Csound

In order to propose a representation of Saffiyu al-Din al-Urmawi's thought concerning ethos and modes, this paper will first model the intervallic system described in his book, then will model modes of his time while focusing on the subtleties of Arabic musical rendition: homophonic aspects and ornamentation. The model proposed in this section will not only serve to understand the working mechanisms of the Arabic music system, but also to reproduce it via Csound in line with an analysis/synthesis perspective or even of emulation8 .

Modeling the intervallic system described by al-Urmawi in Csound

Unlike the transformation Arabic music experienced following the Cairo Congress of Arab Music (1932), especially when a tempered scale of 24 quarter tone was adopted [START_REF] Lambert | Retour sur le congrès de musique arabe du Caire de 1932. Identité, diversité, acculturation : les prémisses d'une mondialisation[END_REF], Saffiyu al-Din al-Urmawi's scale remains authentic and keeps intrinsic idiosyncratic elements of the Arabic musical language. Here is its scale (numerical ratio) and its modes (Figure 2) [6]: This work proposes to use pitch-class in order to model this system in Csound.

Using Pitch-class

In Csound, Pitch can be expressed in frequency or pitchclass. A pitch-class is made of a whole number corresponding to the octave and of a decimal part representing the twelve musical notes in equal temperament (according to the Opcode used). For instance, for a C3, the pitchclass is 8.00, 8.03 for a D#3, 8.04 for an E or 9.00 for a C4. To reproduce a tempered quarter tone on the E note, for instance, simply lower it by half: 8.035 On a non-tempered scale, it only takes to convert the pitch-class of a fundamental note in frequency, to multiply it by the numerical ratio determining the interval then to convert it again in pitch-class.

It must be noted that the conversion of a frequency to a pitch-class in Csound is not direct, unlike the opposite. Here is a code example for the maqâm rahawi:

;

Using function tables:

The ftgen Opcode allows us to create a table referenced by a chosen number that will be named depending on the maqâm involved and which allows us to stock eight pitch-classes corresponding to the musical notes of an octave.

Here is an example of code relative to the maqâm Rahawi:

giRahawi13 ftgen 119, 0, 8, -2, 9.00, irahawisib, irahawilab, irahawisold, irahawifa, irahawimidb, irahawired, 8.00

3.1.3

Classification of modes according to their ethos:

After having modeled the intervallic system described by al-Urmawi, a base with the twelve modes of his time, classified according to their ethos, is obtained (Figure 3) : In order to represent the notion of ethos in Saffiyu al-Din's work, not only do we need to model his intervallic system and modes, but also the Arabic musical rendition, by taking into account its main features such as its homophonic character, its ornamentation and other musical rendition techniques.

Modeling subtleties typical of Arabic musical rendition

The homophonic aspect of Arabic musical rendition

Musical creation in Csound goes through two phases: from instruments of the orchestra, generated by operation codes (Opcodes), and according to events in the score part. We chose to work with the Opcode pluck, which produces a sound with a naturally decaying plucked string, based on the Karplus-Strong algorithms.

Taking the homophonic nature of a musical piece into account means creating successive events9 , belonging to a perspective of horizontal writing.

Ornamentation in Arabic musical rendition

As far as ornamentations are concerned 10 , modeling is done automatically using UDOs (User Defined Opcodes) in order to generate the macro-ornaments 11 .

Below is an example of how a macro-ornament works (Figure 4): The isochrony of events generated by macro-ornaments is done automatically with the metro Opcode . Fundamental notes are notes extracted from the maqâm table on which specific fluctuations must be applied, respecting the macro-ornament framework, which creates four melodic parts. The change of root is done by incrementing an index counter Dynamics and phrasing effects are added to this working principle with decreasing sound intensity and a reproduction of the legato with marked picks in the case of the first and last events.

Starting from a pitch-class in the score, a macroornament runs the contents of the table of a given maqâm. However, if the pitch-class of the score does not correspond to the first pitch-class of the table, the macroornament must generate automatically update the other values.

The semitone function serves to calculate a factor to raise or reduce a frequency by a given number of semitones.

Here is the code of the macro-ornament illustrating the whole operating process described previously:

Control instrument:

The control instrument enables the selection of a UDO depending on the p11 p-field defined in the score. It assigns a value to the name of the macro-ornament in order to be called from the score: It also transfers the entry values to be used and updated.

Below is an extract of code in the control tool:

instr 1 ktrigger init p11 if (ktrigger == 1) then macroornement1 p4, p5, p6, p7, p8, p9, p10, p12, p13 ktrigger = 0 elseif (ktrigger == 2) then macroornement2 p4, p5, p6, p7, p8, p9, p10, p12, p13 ktrigger = 0 ;etc… elseif (ktrigger == 14) then macroornement14 p4, p5, p7, p8, p9, p10, p14 endif endin

The controlled instrument:

Although the instrument controlled in the orchestra plays an execution role, the result of multiple interactivity and conveyance between functional units driven by the controlling instrument, it also plays a more direct execution role. Juggling between these processes serves to accomplish the work. This second role as autonomous instrument favors the melodic sequence, whether ornamented or not, creating as such a kind of fluidity giving a general framework to the work. Here is its source code: Pitch treatment is based on kvib and kgliss with a low frequency oscillator to reproduce fluctuations and glissandos, drawing a series of line segments between the spots specified. The effects of the phrasing are reproduced with an envelope that applies a motive of an attack and of decay as line segments obtained with the Opcode linen.

General operation of the library

The event in the score sends control parameters either to the control instrument that transfers them later to the object library, which updates them and dispatches send to the instrument controlled to perform them with this new data, or directly towards the latter to reproduce the sound occurence (Figure 5):

Conclusion

This paper first dealt with the notion of ethos thanks to a historical approach through the thought of al-Kindi, Ziriab, Ikhwan al-Safa, Avicenna and Saffiyu al-Din al-Urmawi. We underlined the classification of modes according to their ethos in "The book of cycles". In order to feel concretely the modes described by al-Urmawi in the 13th Century and their ethos, a computational model of the non-tempered intervallic system and modes of his times was created in Csound while taking the subtleties of Arabic musical rendition into account. This work will now serve to compose Arabic music according to "The Book of Cycles" in Csound and will enable a direct link between the modes and the notion of ethos described in the 13th Century.

In other words, this library will allow us to rethink the ethos, not only when listening, but also in the composition process.

In the future, the creation of musical demonstrations via this library is considered in order to determine the impact and the feel on groups of listeners to establish a comparative study of the ethos defined by Saffiyu al-Din al-Urmawi.

Figure 1 .

 1 Figure 1. The Bayati mode, its Jins and its variants.

Figure 2 .

 2 Figure 2. Saffiyu al-Din al-Urmawi's modes and intervallic scale

Figure 3 .

 3 Figure 3. Classification of the twelve modes according to their ethos.

Figure 4 .

 4 Figure 4. How macro-ornaments work in Csound

Figure 5 .

 5 Figure 5. General operation of the object library in Csound.

P.D Schuyler also noted that: "In Baghdad, philosophers and mathematicians, like al-Kindi and al-Farabi, began to apply Greek theory to the study of music."

Abu l-Hassan 'Ali Ibn Nafi'(789-857) was a Persian musician, singer, 'ud player and composer.

A society of Arabic philosophers.

Abu Nasr Mohammed ibn Mohammed Farabi (872-950) was a renowned scientist and philosopher of the Islamic Golden Age.

Saffiyu al-Din al-Urmawi (1216-1294) was a renowned musician and writer on the theory of music.

In his book, Al-Urmawi uses the words "adwar" which means cycle and "shadd" which means, literally, to pull. This suggests visualizing intervals on the handle of a string instrument. In this paper, the words maqâm and mode will be used hereinafter.

He proposed the character of Turks, Abyssinians, Blacks and mountain dwellers as examples.

 8 Not only can we consider the simulation of the Arabic music phenomenon but also the possibility of emulation with the aim of recreating or transforming it. The notion of emulation was used and adapted by the AFIM workgroup (Sound Visualization) in an artistic perspective: "Artistic emulation of sound". See activity report proposed by Anne Sedes at: gtv.mshparisnord.org/IMG/pdf/rapportGTVisualisation.pdf

As far as Csound is concerned, event will mean the onset of a sound hereinafter

Acknowledgments

Thanks to Anne Sèdes, my director of research.


Joris Sansen

Romain Bourqui

Patricia Thebault

Julien Allali

David Auber

Visualizing RNA Secondary Structure Base Pair Binding Probabilities using Nested Concave Hulls

Fig. 1. MFE-structure of ncRNA whith potential pairs binding probabilities. From left to right: human, denisovan and ancestral chimp. The two generation parameters are the same for each sample: minimum number of base-pairs binding = 3 and number of levels =10 but the smallest probabilities have been filtered out for Human and Ancestral chimp.

INTRODUCTION

Among all the discovered RNAs, a major part of them are not transformed into proteins, the non-conding RNAs (ncRNAs). Nevertheless, they are involved in many cellular processes and have a variety of catalytic properties. While primary structure of RNAs is a linear sequence of nucleotides, secondary structure refers to strong binding between pairs of nucleotides. This structure is often considered as characteristic of ncRNAs classes and their biological functions. Being able to predict a ncRNA secondary structure therefore helps to predict its functions. Actually, computational biologists have developed methods to compute the probabilities of base-pairing. These probabili-

• Joris Sansen is with Université de Bordeaux, LaBRI, UMR 5800, Talence, FRANCE. E-mail: jsansen@labri.fr. • Romain Bourqui is with Université de Bordeaux, LaBRI, UMR 5800, Talence, FRANCE. E-mail: bourqui@labri.fr. • Patricia Thebault is with Université de Bordeaux, LaBRI, UMR 5800, Talence, FRANCE. E-mail: thebault@labri.fr. • Julien Allali is with Université de Bordeaux, LaBRI, UMR 5800, Talence, FRANCE. E-mail: allali@labri.fr. • David Auber is with Université de Bordeaux, LaBRI, UMR 5800, Talence, FRANCE. E-mail: auber@labri.fr.

ties are then computed to constitute a Minimum Free Energy-structure (MFE) which represents the most likely base pairs binding. These two results are represented within a matrix called a dot-plot. In such matrix, half part represents all the potential base-pairs bindings while the other half contains the results that represent the MFE-structure. Indeed, dot-plots depict the MFE-structure and all potential bindings but it is almost impossible to understand the resulting RNA 2D structure and conclude about its biological functions. Another common technique lies in representing the MFE-structure as a graph. However, the MFE-structure is a prediction that might not depict the reality and that is why the strength of base pairs binding is depicted colors. Nevertheless, such representation does not depict the filtered out potential associations, i.e. the other potential pair bindings which might influence the resulting two-dimensional representation. The challenge 1 of 2015 BIOVIS Design Contest consists in designing a visual representation of the probabilities of base pairs binding. We choose to represent the rejected base pairs using nested concave hulls over the graph of the MFE-secondary structure of the ncRNA. In the following, we introduce our depiction, and detail its design. Then we describe the computational process and provide an analysis of the resulting picture.

Finally we discuss the strengths and weaknesses of our technique and present conclusions.

DESIGN

Our method (see Fig. 1 and Fig. 2) depicts the major unselected potential bases pairs sequences over the RNA secondary structure. Using the bases pairs computed for the Minimum Free Energy structure, we depict the ncRNA secondary structure as a graph. Potential base pairs binding rejected for this structure are then emphasized with the addition of nested concave hulls which put forward the subsequence. The more a sub-sequence is wrapped into hulls, the more its probability is important. Paired sub-sequences are linked to improve the pairs tracking and the bundling algorithm described in [START_REF] Lambert | Winding roads: Routing edges into bundles[END_REF] associated to bezier curve bending is used to improve the design and visibility of the base pairs binding. This makes possible to ease the identification of paired sub-sequences by reducing link crossings and clutter. Finally, we ease the identification of paired subsequences providing a different color to each hulls and corresponding links. Thus, while displaying the MFE ncRNA secondary structure, we made possible to depict the potential base pairs binding and their relative values. Furthermore, our depiction eases the identification of stems and loops that could be transformed if different base pairs binding were selected, or on the contrary those who would remain stable.

DATA PROCESSING

The first step to generate our depiction consists in generating the representation of the ncRNA secondary structure. Nucleotides are laid out using the primary sequence of nucleotides associated to the MFEstructure base pairs binding. This combination makes possible to draw the MFE-structure using a dedicated algorithm as the one described by Auber et al. in [START_REF] Auber | Efficient drawing of rna secondary structure[END_REF]. The second step is the search for the alternative pairs bindings. Our algorithm loop through the dot-plot and seek over the pairing probabilities for diagonal sets of minimum 3 matching bases missing in the computed MFE-structure. This algorithm needs two parameters: i) the minimum number of pairs to search in the set and, ii) the number of different levels of probability required. The second is used to define a threshold which determine the minimum base pair probability value authorized in the set. This threshold is simply defined as the range of probabilities (max-min) divided by the number of levels required. This process is repeated and the threshold progressively increased to pick smaller and smaller sets with a range of probability more and more restrained. Additionnally, we can tune the threshold in order to filter out of the selectable sets the ones with negligible matching probabilities.

The last step consists in the creation of nested concave hulls. The nucleotides of the selected sets are wrapped into hulls onto the MFEstructure and connected with a link to ease the identification of pair-wise elements. The technique used to wrap the sets of nucleotides is the one described by Lambert et al. in [START_REF] Lambert | Visualizing patterns in nodelink diagrams[END_REF]. This technique makes possible to order and nest hulls by size of sets. Thus, even sets with low pairing probabilities can be depicted and identified while putting forward the most likely sequences.

RESULTS

To generate the Fig. 1, we used the previously described generating algorithm with a minimum number of base pairs of 3 and 10 different levels of probabilities. One can easily notice that some regions of the ncRNA present a few other potential base pairs binding indicating a high level of uncertainty, while other regions present just a few potential bindings. Indeed, the two upper stems seem quite uncertain since a few other bases pairing were possible. Moreover, three loops near these stems have an important amount of potential different bindings which could completely change the ncRNA secondary structure. For example, the left stem and its two inner loop could induce the appearance of a larger loop on its extremity by pairing the blue, pink or green sequences. On the contrary, the two bottom stems and loops does not have a lot of other potential nucleotides pairing possibilities. It seems to reveal that the prediction might be correct and correspond to reality.

The generation of this depiction was restrained by the tune of a few parameters which can improve or reduce the readability of the resulting visualization. Indeed, the result of the sequence detection process may vary depending on the two parameters of the generation algorithm: minimum number of base per sequence and value of the minimum probability threshold. Moreover, the bundling process induces variation in the readability of the structure and must be tuned too.

CONCLUSIONS

We have presented here an effective and intuitive depiction of a ncRNA secondary structure showing the most important potential base-pairs binding rejected during the MFE-structure computation. Our method puts forward the different possibilities while emphazing the most probable pair bindings and the influence they may have onto the predicted MFE-structure. The use of nested concave hulls produces a pleasant depiction of the potential base pairs binding which reveals the uncertainty in the predicted MFE-structure and the region of the structure that should remain stable with different base-pairs binding.

Fig. 2 .

 2 Fig. 2. Zoom on a loop of Human ncRNA secondary structure for design details.

ACKNOWLEDGMENTS

We gratefully acknowledge the dataset provided by Maria Beatriz Walter Costa, Henrike Indrischek, Katja Nowick and Christian Hner zu Siederdissen at The University of Leipzig for the purposes of the Bio-Vis 2015 Contest.


Olivier Valentin
email: olivier.valentin@imag.fr

Pierre Lombard

Adrien Lebre

Christian Guinet

Yves Denneulin

Distributed File System for Clusters and Grids

NFSG aims at providing a solution for file accesses within a cluster of clusters. Criteria of easiness (installation, administration, usage) but also efficiency as well as a minimal hardware and software intrusivity have led our developments. By using several facilities such as distributed file systems (NFSP) and a high-performance data transfer utility (GXfer), we hope to offer a software architecture fully compatible with the ubiquitous NFS protocol. Thanks to a distributed storage (especially multiple I/O servers provided by NFSP), several parallel streams may be used when copying a file from one cluster to another within a same grid. This technique improves data transfers by connecting distributed file system at both ends. The GXfer component implements this functionality. Thus, performances only reachable with dedicated and expensive hardware may be achieved.

Introduction

Current trends in High Performance Computing have been characterized by an evolution from the super computing towards cluster computing for several years [1], thanks to an ever-increasing performance/price ratio. As clusters have started to appears in several different places, be it two rooms in a same institute or faraway countries, aggregating the large power of all those newly-born 'poor man's super-computer' has been the source of lots of works (one of the most famous being Globus, which became OGSA project a few years ago).

Such environments have some drawbacks inherent to their qualities : as they offer as lot of services (first-grade authentications, job management, reservations, ldots), they tend to become quite heavy and complex to use. Yet, all those functionalities are not always required to run in dedicated and trusted architectures based on VPN networks (see the French VTHD project 1 ). Clusters evolving in such an architecture, that is clusters linked by means of high-performance links (several gigabit/s), constitutes a 'cluster of clusters' which somewhat heterogeneous characteristics (such as OS, libraries, ...). Thus, to have a useful system, the requirement of easy installation, easy maintainability and adaptability to commodity hardware appeared soon at the conception phase.

Hence, to summarize the features and characteristics we wanted: a common file tree, shared by all the machines within a grid; a minimal access time to data; working efficiently on commodity hardware; aggregation of the unused disk space of clusters; data availability for all the nodes of a cluster; reading/writing of data allowed; NFS protocol [2] and coherency (temporal coherency). To achieve these aims, we have used two tools developed within our team : the first one being a distributed version of the ubiquitous NFS server for clusters, NFSP [3,4], the second one being an inter-cluster transfer tool, GXfer, developed to use efficiently large network pipes without requiring expensive storage systems (SAN based for instance).

After this introductory section, the related works will be shown in section 2. Then, the NFSG principles are shown in section 3, followed by a short evaluation of expected results in section 4. Eventually, a conclusion will give hints about future extensions.

Related Works

A lot of work has been carried out in the file system field, yet the issues of scalability and data sharing within a grid still is a moot point. Within a local site (LAN for instance), the most prominent is most likely to be NFS [2,5] in the Beowulf world, but cannot solve the constraints of the WAN architectures2 . Unlike this latter system, the family of systems constituted by AFS [6], CODA [7] try to address certain issues but ignore some others (consistency, high availability, . . . ) Yet, none of the existing solutions seems adapted to high-performance computing, which often implies the setup of large and expensive machines à la GPFS [8] or more complex and intrusive solutions as what may be seen in the rising Lustre [9]. Setting an open, adaptable and efficient solution is still open to developments. Most of the current works consists in optimizing systems so as to provide better performances within cluster-like architectures. The Globus project [10] and its newer versions (OGSA) tackles grid aspects. In a similar way, our works care about the same constraints but with a released level with regards to the security and try to remain compatible with the established standards.

The NFSG Proposal

NFSG is designed to be a grid file-system. But when we say 'grid', we mean 'cluster of clusters'. In fact, this system should match the needs of several institutions federated into one grid. We think that this strong structure should be taken into account, and thus, that having a two-level system might be a good approach : at the cluster level, a file system that would serve files to local clients, and at the grid level a system to federate the lower level file systems.

NFS4 aims at addressing some of these issues but is still not as widely spread as NFS2 and NFS3.


Pierre Lombard
email: plombard@imag.fr

Yves Denneulin
email: denneuli@imag.fr

Olivier Valentin
email: ovalenti@imag.fr

Adrien Lebre
email: lebre@imag.fr

Improving the Performances of a Distributed NFS Implementation

Our NFS implementation, NFSP (NFS Parallèle) aims at providing some transparent ways to aggregate unused disk space by means of dividing a usually centralized NFS server into smaller entities: a metadata server and I/O servers. This paper illustrates the issues related to increasing the performances of such an implementation. Two different approaches have been taken: distributing the load across several servers and implementing the server in a more efficient and intrusive way (in kernel mode). The results obtained with both versions are given and compared to the ones of the first user-mode implementation.

Introduction

Today's low-cost clusters are often built by using off-the-shelf hardware: each node has its own storage capability, usually only used to store the operating system and the runtime environment. As the hard disk capacity increases, most of the disk space of the nodes remains unused but for temporary files since the users prefer having their files available on every nodes. Partial solutions imply investing in an expensive storage architecture (SAN or RAID servers), yet the disk space is still wasted on the disks of the nodes. Systems providing an aggregation of the unused disk space and the existing ones often implement new protocols or file system types, which may not be considered as a seamless integration for the clients.

Such issues try to be solved by the NFSP project. When the NFSP project was started in mid 2001[1], we chose to use standard and well defined protocols to implement a new kind of NFS server. The first prototype implemented was based on the Linux user-mode server. The first experimental results we got with this implementation highlighted the cost of running the daemon in user-mode. To improve this we tried two methods: balancing the load between several servers and making a more efficient implementation of the server itself. This paper presents these two approaches and compares them from a performance point of view. After this introduction, some related works in the distributed file systems field are shown in section 2. Then the NFSP principles are explained in section 3 and the two methods for improving performances are detailed in sections 4 and 5 which contain performances evaluation. Eventually, some future tracks of research will be tackled in section 6.

Related Works

A large amount of work has been carried out in the network file system since the 1980s. Among the first ones, still used nowadays are Sun NFS and Carneggie Mellon's AFS. NFS is aimed at sharing files among nodes in the same LAN whereas AFS is more suited for WAN architecture. A NFS [2,3] server is made of a node exporting its local file system to the clients who access it through a remote mounting operation. NFS is a stateless protocol, no state is kept on the server side so every operation is self sufficient. This gives NFS some protection against temporary faults. However since the access point is unique for all clients the implementation is inherently centralized and so the storage space is limited to the one on the server. This is not the case for AFS which is a fully distributed file system: servers across different sites cooperate to share the same space and offer all the data they contain to their clients which use as a mounting point a server node part of the global architecture. Contrary to NFS, AFS is a stateful system and so coherency is different from the one found in NFS: when a node opens a file a memory of this operation is kept on the server so when another node access the same file for a write operation a cache invalidation message is sent to all the nodes who opened it. However, this strong coherency implies in high cost in terms of network latency, and thus requires a fast network.

In both cases, the goal of these systems is to provide shared storage for users, which is usually different from the needs of current cluster workloads. Indeed, the development of scientific applications has incurred in new constraints (huge amount of data, level of coherency, fine-grained sharing) on the previous file systems, which led to the design of new storage systems.

A first group of solutions, in order to meet the above needs, might be seen as hardware-based.

File systems such as Sistina's GFS[4] and IBM's GPFS [5] are thought for specialized SAN architectures. Both systems have their data and metadata distributed across the SAN and offer advanced locking and sharing facilities of files. However, the performances of such a system is intimately related to the performances of the storage system underneath. For instance, the GFS handling of coherency relies on an extended SCSI instruction sets. As for GPFS, providing things such as fine-grained coherency by means of software requires a fast and low-latency network like those of the SAN's. Another quite promising new system, LUSTRE, being developed since 2000 [6,7] by ClusterFS Inc. aims at satisfying huge storage and transfers requirements as well as offering a Posix semantics. To achieve these goals, clients, meta-data servers (MDS) and object storage targets(OST)1 are connected by means of a fast network.

Some kind of specialized smart storage.


Ewan Dunbar

Xuan Nga Cao

Juan Benjumea

Julien Karadayi

Mathieu Bernard

Laurent Besacier

Xavier Anguera

Emmanuel Dupoux

THE ZERO RESOURCE SPEECH CHALLENGE 2017

Keywords: zero resource speech technology, subword modeling, acoustic unit discovery, unsupervised term discovery

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Traditional speech technology relies on linguistic expertise and textual information to build acoustic and language models. Recent work has begun to mitigate the need for expert knowledge, having succeeded at training supervised systems using textual transcripts only [START_REF] Hannun | Deep speech: Scaling up end-to-end speech recognition[END_REF][START_REF] Miao | Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding[END_REF]. But for the majority of the world's languages, even textual transcripts are sparse or non-existent, and for many languages, the lack of standard orthography would reduce the utility of such transcripts greatly. This is why there is increasing interest in a radically different approach, the so-called zero resource setting [START_REF] Glass | Towards unsupervised speech processing[END_REF][START_REF] Jansen | A summary of the 2012 JH CLSP Workshop on zero resource speech technologies and models of early language acquisition[END_REF]. The aim is to construct speech systems without any textual or linguistic resource. The fact that young children become fluent communicators in their native language(s) before they learn to read and write, and with minimal or no expert supervision, demonstrates that such a system is possible.

Systems constructed with zero expert resources have at least three kinds of applications. They can form the basis for speech services for the perhaps billions of speakers of languages without readily available linguistic or textual resources in sufficient quantities. They can also help field linguists document endangered languages, by providing tools to semi-automatically analyze and annotate audio recordings using automatically discovered linguistic units (phonemes, lexicon, grammar) [START_REF] Adda | Breaking the unwritten kanguage barrier: The Bulb project[END_REF]. In this case, a realistic scenario is to process raw speech and its translation in a well-resourced language [START_REF] Duong | An attentional model for speech translation without transcription[END_REF][START_REF] Bérard | Listen and translate: A proof of concept for end-to-end speech-to-text translation[END_REF][START_REF] Besacier | Towards speech translation of non written languages[END_REF]. Finally, to the extent that they mimic language development, these models can be used to evaluate scientific claims about development, and act as quantitative tools for psychologists and clinicians interested in the impact of sociolinguistic variations in input on subsequent normal or abnormal language and cognitive development [START_REF] Dupoux | Cognitive science in the era of artificial intelligence: A roadmap for reverseengineering the infant language-learner[END_REF].

As the previous Zero Resource Speech Challenge [START_REF] Versteegh | The zero resource speech challenge 2015[END_REF][START_REF] Versteegh | The zero resource speech challenge 2015: Proposed approaches and results[END_REF], the 2017 installment consists of two different subchallenges. Participants can submit systems that do either subword modelling (track one) or spoken term discovery (track two). Subword modelling means constructing a representation of speech sounds; we say "subword" instead of "phoneme" or "sound" because the criterion for success does not demand any particular temporal window size. The requirement is to have a representation of the speech signal that is robust to talker variation, and makes the distinctions necessary to distinguish words. Spoken term discovery means discovering recurring speech fragments in the audio: the system must output time-stamps delimiting stretches of speech, associated with class labels, ideally corresponding to real words in the language. The systems must be able to do their task without linguistic resources. The 2017 Zero Resource Speech Challenge1 has two main innovations compared to the 2015 iteration: (1) it tests how well systems' architectures and hyperparameters generalize to unseen languages (the systems are developed with three languages and tested on two 'surprise' languages); (2) it tests how well the trained systems' parameters adapt to unseen talkers (varying the amount of speech available for each talker).

THE CHALLENGE

Track one: Subword modelling

The goal of subword modelling is to learn representations appropriate to the sounds of the language (phones or phonemes). As adults, we easily perceive differences between individual phonemes in our native language, substantially better than in non-native languages [START_REF] Lisker | A crosslanguage study of voicing in initial stops: Acoustical measurements[END_REF]. This so-called perceptual tuning develops substantially during the first year of life [START_REF] Janet | Cross-language speech perception: Evidence for perceptual reorganization during the first year of life[END_REF][START_REF] Kuhl | Human adults and human infants show a perceptual magnet effect for the prototypes of speech categories, monkeys do not[END_REF].

The input to subword modelling is a set of acoustic features encoding continuous speech. The result is a transformation that applies to new samples of running speech, yielding a new representation (continuous or discrete) of that speech. After training in a particular language from unlabelled speech, the representation is computed for a test set in the same language on novel speakers, to be evaluated on phoneme discrimination based on gold-standard annotations.

Track two: Spoken term discovery

Intuitively, the goal of spoken term discovery is to find words in the speech stream-just as the infant learns the words of its language by listening. The input to candidate systems is a series of speech features. The output is a set of boundaries delimiting the start and end of proposed word tokens discovered in the speech, and category labels indicating proposed word types. These boundaries may, but need not, constitute an exhaustive parse of the speech. The evaluation we apply is a set of scores measuring different aspects of the alignment with the words in the gold-standard transcription. As is customary in the field of word segmentation, we do not provide a separate test set for this track; we rely on the surprise languages to assess possible hyperparameter overfitting.

Generalization across languages

This challenge is explicitly set up as a 'learning to learn' or meta-learning problem: participants select the best architectures and tune their hyperparameters on three development languages on which the evaluation software is provided for a test set. The higher level test is performed on two new surprise languages for which the evaluation is withheld. Participants submit the output of their previously optimized systems for these two corpora, and the organizers perform the evaluation. Each submission (maximum five per team) is automatically evaluated and made public.

Adaptation to new speakers

The subword modeling task is explicitly set up as a talker adaptation problem. The training data is constructed from a skewed distribution of speakers, and the test set consists only of new speakers appearing in files of various durations (from 1 second to two minutes). This enables us to evaluate models' ability to generalize and adapt to new speakers on the basis of limited evidence.

DATA SETS

Two groups of data sets are provided as part of the challenge: the development data and the surprise data. The development data consists of corpora from three different languages (English, French and Mandarin). Each corpus comes with software that does the evaluation for the two tracks. Challenge participants are encouraged to use these resources to tune their hyperparameters using a cross validation approach to maximize generalizability. Each corpus is split into a train and a test set; the subword modelling evaluations are run on the test sets, and the spoken term discovery evaluations on the training sets. The participants then have to submit their systems and their output on all the data sets for independent evaluation (run automatically upon submission). The surprise data consists of corpora from two new languages, the identity of which was not revealed to participants. Participants receive only the speech corpora, with no additional resources. As before, each contains a training set (which participants can use to train their systems) and a test set for track one evaluation.

The amount of data in the training part of the development data sets varies from 2.5 to 45 hours (see Table 1), to ensure that systems can work both with limited data and with large data sets. The statistics of the two surprise languages fall between these two extremes. The distribution of speakers in the training sets is shaped to reflect what is typically found in natural language acquisition settings: there is a "family"-a small number of speakers that make up a large proportion of the total speech-and a set of "outsiders"-a larger number of speakers that each appear in smaller proportions. The test sets consist of many short files, are organized into subsets of differing length (1s, 10s and 120s).

The English and French corpora were taken from the Lib-riVox collection of audio books2 and phone force-aligned using Kaldi [START_REF] Povey | The kaldi speech recognition toolkit[END_REF]. The Mandarin corpus is the one described in [START_REF] Wang | Thchs-30: A free chinese speech corpus[END_REF], force-aligned using Kaldi. The first surprise language is German, and was taken from LibriVox and force-aligned using Kaldi as well. The second surprise language is Wolof, and is the corpus described in [START_REF] Gauthier | Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of Wolof[END_REF].

EVALUATION

Evaluation of subword modelling

Unsupervised subword modeling is a representation learning problem: the system must find speech features that emphasize linguistically relevant properties (phoneme structure) and de-emphasize the linguistically irrelevant ones (speaker identity, emotion, channel, etc). We use a minimal pair ABX task [START_REF] Schatz | Evaluating speech features with the Minimal-Pair ABX task (I): Analysis of the classical MFC/PLP pipeline[END_REF][START_REF] Schatz | Evaluating speech features with the Minimal-Pair ABX task (II): Resistance to noise[END_REF], which does not require any training, and only requires a dissimilarity metric between speech tokens. The ABX task is inspired by match-to-sample tasks used in human psychophysics and is a simple way to measure discriminability between two sound categories (where the sounds A and B belong to different categories x and y, respectively, and the task is to decide whether the sound X belongs to one or the other). We define the ABX-discriminability of category x from category y as the probability that A and X are further apart than B and X when A and X are from category x and B The dissimilarities provided in this challenge are based on dynamic time warping, the underlying frame-level dissimilarity being either the cosine distance or KL-divergence. For most systems (signal processing, embeddings) the cosine distance usually gives good results, and for others (posteriorgrams) the KL divergence is more appropriate. Participants are allowed to supply their own dissimilarity as long as it was not obtained through supervised training.

We focus on phone triplet minimal pairs: sequences of 3 phonemes that differ in the central sound (not necessarily real words, e.g., "beg"-"bag", "api"-"ati", etc). Our compound measure sums over all minimal pairs of this type found in the corpus in a structured manner, that depends on the task. For the within-speaker task, all of the phone triplets belong to the same speaker (e.g. A = beg T 1 , B = bag T 1 , X = bag T 1 ). The scores for a given minimal pair are first averaged across all of the speakers for which this minimal pair exists. The resulting scores are then averaged over all found contexts for a given pair of central phones (e.g. for the pair /a/-/e/, average the scores for the existing contexts such as /b g/, /r d/, /f s/, etc.). Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, A and B belong to the same speaker, and X to a different one. A = beg T 1 , B = bag T 1 , X = bag T 2 . The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.

Evaluation of spoken term discovery

"Spoken term discovery" is a broad label. We use it to refer to systems that do one or more of three tasks. Systems that do matching find fragments that are matched pairwise as being instances of the same sequence of phonemes, attempting to find as many as possible. They can be evaluated based on how similar the matched fragments are, and how much of the corpus they cover. Systems that do lexicon discovery group these fragments into sets (rather than just matching pairwise), with the goal of finding a high-quality lexicon of types. They can be evaluated based on how well the sets match on the sequence of phonemes, and how well the sets match the goldstandard lexicon of word types. Systems that do word segmentation attempt to find fragments that are aligned with the gold-standard word transcription. By setting out three different types of criteria, the intention is to be open to various types of "spoken term discovery" systems, all of which in some sense "find words." The result is that we do three (non-independent) types of evaluations in track two. All of these evaluations are done at the level of the phonemes. Using the aligned phoneme transcription, we convert any discovered fragment of speech into its transcribed string. If the left or right edge of the fragment contains part of a phoneme, that phoneme is included in the transcription if it corresponds to more than more than 30ms or more than 50% of its duration.

Evaluation as matching system

The evaluation of spoken term discovery systems as matching systems consists of two scores, NED and coverage. NED, is the average, over all matched pairs, of the Levenshtein distance between their phonemic transcriptions, divided by the max of their phonemic length. The second score is the coverage, the fraction of the discoverable part of the corpus that is covered by all the discovered fragments. The discoverable part of the corpus is found by computing the union of all of the intervals corresponding to all of the pairs of ngrams (with n between 3 and 20). This is almost all of the corpus, except for unigram and bigram hapaxes.

Evaluation as lexicon discovery system

Six scores are used to evaluate the performance of a spoken term discovery system in terms of lexicon discovery. The first three are grouping precision, recall and F-score. These are defined in terms of P clus , the set of all pairs of fragments that are groupes in the same cluster, and P goldclus , the set of all non-overlapping pairs of fragments which are both discovered by the system (not necessarily in the same cluster) and have exactly the same gold transcription.

Prec:

t∈types(P clus ) w(t, Pclus) |occ(t, Pclus ∩ Pgoldclus)| |occ(t, Pclus)| (1)
Rec:

t∈types(P goldclus ) w(t, Pgoldclus) |occ(t, Pclus ∩ Pgoldclus)| |occ(t, Pgoldclus)| (2)
Where t ranges over the types of fragments (defined by the transcription) in a cluster C, and occ(t, C) is the number of occurrences of that type, w the number of occurrences divided by the size of the cluster. In other words, Prec is a weighted measure of cluster purity and Rec, of the inverse of the cluster's fragmentation. The other three scores are type precision, recall, and F-score. Type precision is the probability that discovered types belong to the gold set of types (real words), whereas type recall is the probability that gold types are discovered. We restrict both sets to words between three and twenty segments long.

Evaluation as word segmentation system

Six scores are used to evaluate the performance of a spoken term discovery system as a system for word segmentation.

The first three are token precision, recall, and F-score. Token precision is the probability that discovered fragment tokens are in the gold set of word fragments, and token recall the probability that the gold fragments are discovered. Again, tokens are restricted to be between three and twenty segments long. Finally, boundary precision, recall, and F-score are similarly defined with respect to the set of discovered and gold word boundaries.

DESCRIPTION OF SUBMITTED SYSTEMS

We received sixteen systems for track one, and three for track two. For two track one submissions, the authors declared that the systems were not intended as submissions, and are thus not included in the present overview, although the results can be found on the leaderboard on the Challenge website (www. zerospeech.com/2017).

Track 1

The baseline features are the MFCC features provided to participants; they are evaluated using the ABX score using the cosine similarity. The topline system is a supervised HMM-GMM phone recognition system, with a bigram language model, trained with a Kaldi recipe [START_REF] Povey | The kaldi speech recognition toolkit[END_REF]. The phone posteriors are evaluated using the KL divergence.

The submitted systems follow four strategies. The first strategy consists in performing a bottom-up frame-level clustering, following up on the success of [START_REF] Chen | Parallel inference of Dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study[END_REF] with this simple strategy. In system [H], from Heck et al. [START_REF] Heck | Feature optimized dpgmm clustering for unsupervised subword modeling: A contribution to zerospeech 2017[END_REF], PLP representations are used, as well as the output of three learned feature transformations, to be the input to the same frame-level clustering algorithm used in [START_REF] Chen | Parallel inference of Dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study[END_REF] (the parallel Dirichlet process Gaussian mixture model sampler of [START_REF] Chang | Parallel sampling of DP mixture models using sub-cluster splits[END_REF]). The transformations (LDA, LDA+MLLT; LDA+MLLT+fMLLR) are aimed at making the representation talker invariant. The training requires labels, which are obtained from an initial clustering using the DPGMM algorithm. The submitted system is a combination of the raw posteriorgrams (not smoothed by any language model) from the four DPGMMs obtained by clustering frames in each of the three transformed representations, plus the raw PLP features. System [P1], from Pellegrini et al. [START_REF] Pellegrini | Unsupervised discovery of sub-lexical units in speech based on ZCA and k-means[END_REF], uses k-means clustering on ZCA-transformed (whitened using PCA and rotated back to the original axes) MFCC features. In system [P2], this is followed by a step of centroid re-estimation: the centroids are re-estimated based only on the data points for which the label is the same as that of its left and right neighbors. The distance from point to all cluster centroids is the feature vector in both.

A second strategy constructs language-independent embeddings by training neural networks in an unsupervised way. The systems of Chen et al. [START_REF] Chen | Multilingual bottle-neck feature learning from untranscribed speech[END_REF] first cluster frames separately on each language, using the same DPGMM algorithm as [START_REF] Heck | Feature optimized dpgmm clustering for unsupervised subword modeling: A contribution to zerospeech 2017[END_REF] and [START_REF] Chen | Parallel inference of Dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study[END_REF]. The cluster labels are then used as tar-gets for a multitask neural network, where the loss function is the mean of the five cross-entropy losses. A linear bottleneck layer in the network is the final representation. System [C1] is trained on MFCCs; [C2] transforms the input features using unsupervised linear VTLN. The three systems in Ansari et al. [START_REF] T K Ansari | Deep learning methods for unsupervised acoustic modeling: LEAP submission to ZeroSpeech challenge 2017[END_REF] combine two sets of features, also trained on all five languages. The first set of features is always a high-dimensional hidden layer from an autoencoder trained on the MFCC frames. The second set of features is a hidden layer from a network trained to predict unsupervised frame labels. These labels are obtained by training a Gaussian mixture model on the speech frames. In system [A1], the cluster labels are used as targets for the DNN trained with the MFCCs as input. System [A2] instead uses time-smoothed frame labels as decoded by a large Gaussian-mixture-HMM (one state for each of the initial cluster labels), in which states have a high self-transition probability. System [A3] does the same but trains the DNN with the autoencoder features as inputs.

In systems [A1] and [A2], the DNN features are combined with the autoencoder features in the dissimilarity function for the ABX task: the dissimilarity is a weighted average between the cosine DTW distance using the autoencoder features, and the cosine DTW distance using the other set of features (the weights are optimized on the development languages and not reported). An additional system is described in [START_REF] T K Ansari | Unsupervised HMM posteriograms for language independent acoustic modeling in zero resource conditions[END_REF], system [A4], which consists of the DNN features from system [A2].

The third strategy is to use spoken term discovery to improve the acoustic features, following [START_REF] Thiolliere | A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling[END_REF][START_REF] Renshaw | A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge[END_REF]. The system of Yuan et al. [START_REF] Yuan | Extracting bottleneck features and word-like pairs from untranscribed speech for feature representations[END_REF] generates bottle-neck features in the same way as [START_REF] Chen | Multilingual bottle-neck feature learning from untranscribed speech[END_REF], but then applies the STD system of [START_REF] Jansen | Efficient spoken term discovery using randomized algorithms[END_REF] to discover pairs of matched acoustic motifs, which are then used to further train the features. Specifically, following [START_REF] Renshaw | A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge[END_REF], a stacked autoencoder is trained on DTW-aligned frame pairs from instances of motifs paired by the STD system. System

[Y1] uses pairs obtained by applying the STD system (only) to the English training data. System [Y2] (not reported in [START_REF] Chen | Multilingual bottle-neck feature learning from untranscribed speech[END_REF]) obtained pairs from all corpora of all five languages. System [YS] is a supervised comparison, using transcribed pairs from the Switchboard corpus rather than STD.

The strategy of Shibata et al. [START_REF] Shibata | Composite embedding systems for ZeroSpeech2017 track 1[END_REF] is to use supervised training on out-of-domain languages. In [S1], the features are bottleneck features from a neural network acoustic model trained supervised on Japanese as part of an HMM. In [S2], an end-to-end convolutional network plus bidirectional LSTM is trained using corpora from ten languages, and features from a hidden layer are combined with the bottleneck Japanese features. This second scheme can only be considered out of domain in a weak sense: new corpora are used, but the ten additional languages include English, Mandarin, and German.

Track 2

The baseline of track two was computed using [START_REF] Jansen | Efficient spoken term discovery using randomized algorithms[END_REF], which does pair-matching using locally sensitive hashing applied to PLP features and then groups pairs using graph clustering. The parameters of the STD stayed the same across all languages, except that the DTW threshhold was increased for Mandarin (to 0.90, from 0.88) in order to obtain a NED value similar to that of other languages. The topline was an exhaustive-parsing word segmentation model based on the textual transcriptions (a unigram grammar trained in the adaptor grammar framework: [START_REF] Goldwater | A bayesian framework for word segmentation: Exploring the effects of context[END_REF]).

System [K], from Kamper et al. [START_REF] Kamper | An embedded segmental k-means model for unsupervised segmentation and clustering of speech[END_REF] uses k-means to discover recurring acoustic patterns, jointly optimized with an exhaustive segmentation. The variable-length acoustic chunks of speech yielded by a segmentation are reduced to fixed dimension by down-sampling in the time dimension for clustering by k-means. The systems of García-Granada et al. [START_REF] García-Granada | ZeroSpeech2017 ELIRF-UPV system[END_REF] use a supervised ASR system for Hungarian to decode the speech. Chunks of (transcribed) speech match if they have the same transcription. These matches are then filtered: a representation of the speech is obtained by training an autoencoder, and only pairs with DTW sufficiently low in this representational space (below a threshhold) are retained (system [G1]). In system [G2], pairs are tolerated that have a one-phone difference in their transcriptions.

RESULTS

The results of track one are summarized in Table 2. The clear winner among the unsupervised models is [H]. While the paper focuses on the role of learned feature transformationsand demonstrates that they improve the evaluation-these are unlikely to be solely responsible for the very good performance of the system. The baseline system described in the paper, with no transformations applied, consisting of posteriorgrams from a frame-wise DPGMM clustering, still performs extremely well. Although it was not evaluated on the surprise languages, its average ABX score on the development languages (9.7) is still better that of the second-ranked system [C2] (10. ) use an initial stage of monolingual training, followed by multilingual training. Among the other systems in [START_REF] Chen | Multilingual bottle-neck feature learning from untranscribed speech[END_REF] (not submitted for final evaluation) are some with fully monolingual training, which serve to partly evaluate the effect of multilingual training: monolingual training fares better, within language. However, as the numerical scores are not presented, it is not possible to determine whether the use of monolingual training could account for the good scores of Heck et al. What is clear is that multilingual training still works reasonably well-all the remaining systems, except for the lower performing system [P], use multilingual training, and show substantial improvements over the baseline. The setting for these systems corresponds to the situation of an infant raised in a multilingual environment.

The STD strategy for improving subword features fares relatively poorly; a limitation of the Yuan et al. systems is that they use an architecture (autoencoders) that was previously reported to be suboptimal for improving acoustic features [START_REF] Renshaw | A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge[END_REF]. A Siamese network [START_REF] Thiolliere | A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling[END_REF] might improve the result.

The results of track two are in Table 3. As in the previous challenge, the baseline provided by the system of [START_REF] Jansen | Efficient spoken term discovery using randomized algorithms[END_REF] has a high NED (very good matching), but has a low coverage (few matches). Given that system [K] demands an exhaustive parse, it is guaranteed to have full coverage, which risks poorer matches. It is thus notable that system [K] has a NED on the development languages comparable to the partly supervised systems [G1] and [G2]. On the other hand, grouping by supervised transcription clearly works better than k-means clustering; while the groups have low precision (many erroneously grouped items) for both kinds of systems, [G1]/ [G2] have much higher grouping recall (more of the true group members among the items grouped together) than system [K]. The quality of segmentation, in terms of tokens and of boundaries, is, on the other hand, much better in system [K], presumably as a result of increased coverage.

CONCLUSION

The results of the 2017 Zero Resource Speech Challenge build very clearly on the previous challenge. The strategies most successful in the previous challenge for track one, bottom-up clustering and training based on pairs of matching words discovered using spoken term discovery-are built upon, and a new strategy, multilingual training, is introduced. This strategy, although not completely foreseen by the organizers, would be similar to the situation of a multilingual infant. This new approach is used in conjunction with additional tools, including further training using neural networks and the application of time series models. Interestingly, simple bottom-up clustering still seems to work best in the unsupervised case. The fact that supervised labels trained fully out of domain seem to yield good results in both track one and track two (on Japanese, in the case of [S1], and on Hungarian in the case of [G1] and [G2]) indicates that the approach of having systems with strong prior knowledge about human speech is still a benchmark to be beaten in the unsupervised case. (The excellent results of [S2] may yet be partly attributable to the use of supervised training in German, which was one of the surprise languages.) Further work is needed to systematically evaluate the effect of each of these different manipulations, however, and to directly compare these new approaches with those from the previous challenge.

Potential future lines of inquiry building on the new track two systems might include exploring using these systems to feed subword modelling, especially in combination with some of the new subword modelling strategies used here, in light of the successful use of STD to improve subword models in the previous challenge, and the reasonable improvement over baseline shown in this challenge. In the other direction, the major unsupervised improvements to the speech features demonstrated in track one suggest strong room for improvement in unsupervised spoken term discovery systems like [K].

The 2017 ZeroSpeech Challenge remains open for submissions exploring these, or any other new strategies for discovering phoneme-and word-like units without access to linguistic resources.

  7), from Chen et al. The systems presented by Heck et al. all have in common monolingual training, direct evaluation of GMM posteriorgrams, and the fact that they use PLP features as input, rather than MFCCs. The systems of Chen et al. (as well as Yuan et al.

Table 1 .

 1 Corpus statistics

					Training				Test
			Relatives		Outsiders		Total
		#speakers duration/ speaker #speakers duration/ speaker duration #words number of files duration (min)
	Development
	English	9	165-220min	60	10min	45h	370k	30658	1634
	French	10	110-195min	18	10min	24h	220k	23765	1061
	Mandarin	4	20-25min	8	10min	2h30	20k	25383	1522
	Surprise
	S1 -German	10	85-150min	20	10min	25h	213k	15243	687
	S2 -Wolof	4	37-42min	10	10min	4h	31k	7201	354

Table 2 .

 2 Track 1 within and between talker ABX scores for three test files durations, for the development (average) and surprise languages, for the 14 submitted systems (the mean performance on the surprise set is in square brackets). The three supervised systems are listed separately. See http://zerospeech.com/2017 for the full table including development languages.

	Avg. Development 1 s 10 s 2 min 1 s 12 12.1 12.1 14.2 13.3 12.9 S1 -German 10 s 2 min 1 s 14.1 14.3 14.1 S2 -Wolof 10 s 2 min Between 23.3 23.4 23.3 Baseline [18.84] Within 27.5 27.3 27.1 30 29.5 29.5 Topline [7.19] Within 8 5.4 5.3 8.7 7.1 7.0 6.6 4.6 3.4 Between 9.6 7.2 6.9 12.8 10.5 10.4 7.1 3.6 4.3 H [9.16] Within 8.5 7.6 7.4 6.5 5.6 5.3 10.9 8.8 8.4	Avg. Development 1 s 10 s 2 min 1 s 8.8 7.8 7.7 6.8 Between 14.5 13.4 13.3 A2 [11.95] Within 16.0 14.0 13.9 S1 -German 10 s 2 min 1 s 6.0 6.0 10.1 9.6 S2 -Wolof 10 s 2 min 9.6 17.9 16.9 16.6 A3 [11:93] Within 9.5 8.3 8.2 7.3 6.2 6.1 11.1 10.3 10.2 Between 14.5 13.3 13.2 15.5 13.5 13.4 17.6 16.0 16.0 A4 [12.17] Within 9.7 8.5 8.5 7.6 6.4 6.2 11.6 10.9 10.7
		Between 10.8 9.3	9.0	11.9 10.0 9.7	13.0 10.0 9.9	Between 14.5 13.3 13.2	15.7 13.7 13.5	17.5 16.1 16.1
	P1 [14.96]	Within	10.9 8.8	8.7	8.9	6.7	6.4	13.3 11.9 11.8	Y1 [12.43] Within	10.8 8.4	8.4	8.1	6.0	6.0	12.6 10.0 9.9
	P2 [14.9]	Between 17.5 15.8 15.7 Within 10.8 8.7 8.5	19.4 16.2 15.9 8.8 6.6 6.3	22.8 23.1 23.1 13.1 11.7 11.7	Between 15.3 13 Y2 [12.29] Within 10.7 8.4	12.6 8.2	16.2 12.9 12.6 8.2 6.2 6.2	19.5 17.1 16.6 12.7 10.1 9.9
		Between 17.5 15.8 15.7	19.2 16.3 16.0	23.3 23.3 23.1	Between 15.1 12.7 12.4	16.4 13.3 13.0	19.2 17.3 16.7
	C1 [11.95] C2 [11.24] A1 [12.3]	Within Between 14.5 12.8 12.5 10.0 8.4 8.3 Within 10.1 8.5 8.4 Between 13.9 11.9 11.7 Within 8.8 7.9 7.8 Between 15.2 14.2 14.0	7.6 15.5 12.9 12.7 6.22 6.3 7.6 6.2 6.1 14.7 11.7 11.6 6.9 6.1 6.0 16.9 14.7 14.7	11.7 9.9 17.6 16.9 16.3 9.8 11.6 9.8 9.6 16.9 14.7 14.4 9.9 9.2 9.1 18.8 17.7 17.7	YS [12.32] Within Between 14.7 12.3 12.1 10.7 8.3 8.1 S1 [8.54] Within 8.7 8.3 7.3 Between 11.4 10.4 9 S2 [6.99] Within 7.1 6.7 6.3 Between 9.0 8.6 7.8	8.0 15.8 12.4 12.3 6.0 5.9 6.3 5.8 5.0 11.6 9.9 8.7 5.2 4.9 4.5 9.3 8.6 7.8	12.9 10.8 10.6 18.7 17.4 17.0 9.0 8.7 7.2 11.5 10.2 8.6 6.9 7.0 6.3 8.3 7.9 7.2

is from category y, according to a dissimilarity funcion d. We obtain a symmetric discriminability score by taking the average of the ABX discriminability of x from y and of y from x.

http://zerospeech.com/2017

http://librivox.org/

Table 3. Track 2 results. Scores are averages over evaluations done on subparts of the corpus: the list of corpus gold utterances is split in groups of 1000 elements/utterances in order to compute the scores in parallel. Thus, the F -scores presented are not directly related to the precision and recall scores.