Proceedings of the Third Symposium on Programming Languages and Software Tools : Kääriku, Estonia, August 23-24 1993

http://www.ester.ee/record=b1064507*es

Automated Testing of Speech-to-Speech Machine Translation in Telecom Networks

Globalisoituvassa maailmassa kyky kommunikoida kielimuurien yli käy yhä tärkeämmäksi. Kielten opiskelu on työlästä ja siksi halutaan kehittää automaattisia konekäännösjärjestelmiä. Ericsson on kehittänyt prototyypin nimeltä Real-Time Interpretation System (RTIS), joka toimii mobiiliverkossa ja kääntää matkailuun liittyviä fraaseja puhemuodossa kahden kielen välillä. Nykyisten konekäännösjärjestelmien suorituskyky on suhteellisen huono ja siksi testauksella on suuri merkitys järjestelmien suunnittelussa. Testauksen tarkoituksena on varmistaa, että järjestelmä säilyttää käännösekvivalenssin sekä puhekäännösjärjestelmän tapauksessa myös riittävän puheenlaadun. Luotettavimmin testaus voidaan suorittaa ihmisten antamiin arviointeihin perustuen, mutta tällaisen testauksen kustannukset ovat suuria ja tulokset subjektiivisia. Tässä työssä suunniteltiin ja analysoitiin automatisoitu testiympäristö Real-Time Interpretation System -käännösprototyypille. Tavoitteina oli tutkia, voidaanko testaus suorittaa automatisoidusti ja pystytäänkö todellinen, käyttäjän havaitsema käännösten laatu mittaamaan automatisoidun testauksen keinoin. Tulokset osoittavat että mobiiliverkoissa puheenlaadun testaukseen käytetyt menetelmät eivät ole optimaalisesti sovellettavissa konekäännösten testaukseen. Nykytuntemuksen mukaan ihmisten suorittama arviointi on ainoa luotettava tapa mitata käännösekvivalenssia ja puheen ymmärrettävyyttä. Konekäännösten testauksen automatisointi vaatii lisää tutkimusta, jota ennen subjektiivinen arviointi tulisi säilyttää ensisijaisena testausmenetelmänä RTIS-testauksessa.In the globalizing world, the ability to communicate over language barriers is increasingly important. Learning languages is laborious, which is why there is a strong desire to develop automatic machine translation applications. Ericsson has developed a speech-to-speech translation prototype called the Real-Time Interpretation System (RTIS). The service runs in a mobile network and translates travel phrases between two languages in speech format. The state-of-the-art machine translation systems suffer from a relatively poor performance and therefore evaluation plays a big role in machine translation development. The purpose of evaluation is to ensure the system preserves the translational equivalence, and in case of a speech-to-speech system, the speech quality. The evaluation is most reliably done by human judges. However, human-conducted evaluation is costly and subjective. In this thesis, a test environment for Ericsson Real-Time Interpretation System prototype is designed and analyzed. The goals are to investigate if the RTIS verification can be conducted automatically, and if the test environment can truthfully measure the end-to-end performance of the system. The results conclude that methods used in end-to-end speech quality verification in mobile networks can not be optimally adapted for machine translation evaluation. With current knowledge, human-conducted evaluation is the only method that can truthfully measure translational equivalence and the speech intelligibility. Automating machine translation evaluation needs further research, until which human-conducted evaluation should remain the preferred method in RTIS verification

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

A lossy, dictionary -based method for short message service (SMS) text compression

Short message service (SMS) message compression allows either more content to be fitted into a single message or fewer individual messages to be sent as part of a concatenated (or long) message. While essentially only dealing with plain text, many of the more popular compression methods do not bring about a massive reduction in size for short messages. The Global System for Mobile communications (GSM) specification suggests that untrained Huffman encoding is the only required compression scheme for SMS messaging, yet support for SMS compression is still not widely available on current handsets. This research shows that Huffman encoding might actually increase the size of very short messages and only modestly reduce the size of longer messages. While Huffman encoding yields better results for larger text sizes, handset users do not usually write very large messages consisting of thousands of characters. Instead, an alternative compression method called lossy dictionary-based (LD-based) compression is proposed here. In terms of this method, the coder uses a dictionary tuned to the most frequently used English words and economically encodes white space. The encoding is lossy in that the original case is not preserved; instead, the resulting output is all lower case, a loss that might be acceptable to most users. The LD-based method has been shown to outperform Huffman encoding for the text sizes typically used when writing SMS messages, reducing the size of even very short messages and even, for instance, cutting a long message down from five to two parts. Keywords: SMS, text compression, lossy compression, dictionary compressio

Performance Evaluation of LTE and LTE advanced standards for next generation mobile networks

Nel corso della trattazione sono analizzati gli standard 3GPP LTE e LTE-Advanced per la prossima generazione delle reti mobili cellulari. L'algoritmo OptiMOS, che può essere impiegato dalla Stazione Base per servire in modo efficiente connessioni VoIP, è descritto nel capitolo [8]. L’algoritmo di link scheduling Relay, finalizzato a ottimizzare le reti LTE avanzate in presenza di nodi relay è descritto nel capitolo [9]. Questo lavoro è stato presentato in adempimento parziale dei requisiti per la Laurea di Dottore di Ricerca in Ingegneria dell'Informazione presso l'ufficio informazioni Dipartimento di Ingegneria dell'Università degli Studi di Pisa, Italia