Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping

Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping

Text to Speech in New Languages without a Standardized Orthography

Abstract Many spoken languages do not have a standardized writing system. Building text to speech voices for them, without accurate transcripts of speech data is difficult. Our language independent method to bootstrap synthetic voices using only speech data relies upon cross-lingual phonetic decoding of speech. In this paper, we describe novel additions to our bootstrapping method. We present results on eight different languages---English, Dari, Pashto, Iraqi, Thai, Konkani, Inupiaq and Ojibwe, from different language families and show that our phonetic voices can be made understandable with as little as an hour of speech data that never had transcriptions, and without many resources in the target language available. We also present purely acoustic techniques that can help induce syllable and word level information that can further improve the intelligibility of these voices. Index Terms: speech synthesis, synthesis without text, languages without an orthography Introduction Recent developments in speech and language technologies have revolutionized the ways in which we access information. Advances in speech recognition, speech synthesis and dialog modeling have brought out interactive agents that people can talk to naturally and ask for information. There is a lot of interest in building such systems especially in multilingual environments. Building speech and language systems typically requires significant amounts of data and linguistic resources. For many spoken languages of the world, finding large corpora or linguistic resources is difficult. Yet, these languages have many native speakers around the world and it would be very interesting to deploy speech technologies in them. Our work is about building text-to-speech systems for languages that are purely spoken languages: they do not have a standardized writing system. These languages could be mainstream languages such as Konkani (a western Indian language with over 8 million speakers), or dialects of a major language that are phonetically quite distinct from the closest major language. Building a TTS system usually requires training data consisting of a speech corpus with corresponding transcripts. However, for these languages that aren't written down in a standard manner, one can only find speech corpora. Our current efforts focus on building speech synthesis systems when our training data doesn't contain text. It may seem futile to build a TTS system when the language at hand doesn't have a text form. Indeed, if there is no text at training time, there won't be text at test time, and then one might wonder why we need a TTS system at all. However, consider the use case of deploying a speech-tospeech translation of video lectures from English into Konkani. We have to synthesize speech in this "un-written" language from the output of a machine translation system. Even if the language at hand may not have a text form, we need some intermediate representation that can act as a text form that the machine translation system can produce. A first approximation of such a form is phonetic strings. Another use case for which we need TTS without text is, say, deploying a bus information system in Konkani. Our dialog system could have information about when the next bus is, but it has to generate speech to deliver this information. Again, one can imagine using a phonetic form to represent the speech to be generated, and produce a string of phones from the natural language generation model in the bus information dialog system. The work we present here is our continued effort in improving text to speech for languages that do not have a standardized orthography. We have built voices for several languages, from purely speech corpora, and produced understandable synthesis. We use cross-lingual phonetic speech recognition methods to do so. Phone strings are not ideal for TTS, however, as a lot of information is contained in higher level phonological units including the syllables and words that can help produce natural prosody. However, detecting words from speech corpus alone is a difficult task. We have explored how purely acoustic techniques can be used to detect word like units in our training speech corpus and use this to further improve the intelligibility of speech synthesis

REVIEW OF THE EVOLUTION OF THE TECHNOLOGY OF AUTOMATIC MACHINE TRANSLATION

Automatsko strojno prevođenje postalo je nezamjenjiv dio velikog broja organizacija koje posluju u međunarodnom okruženju i koje imaju potrebu generirati velike količine prijevoda za svoju dokumentaciju. Strojno prevođenje danas se smatra jednom od neizostavnih disruptivnih tehnologija koja uvelike doprinose cjelovitoj transformaciji poslovnih procesa u segmentu prevođenja tekstova napisanih na prirodnom jeziku. Ideja iza strojnog prevođenje je omogućiti automatizaciju barem dijela procesa prevođenja, posebno kada je riječ o velikoj količini podataka, ne bi li se ubrzalo cjelokupno poslovanje jedne organizacije i time se ostvarila konkurentska prednost na tržištu koje se brzo mijenja i kojemu se brzo treba prilagoditi. No, razvoj tehnologije automatskog strojnog prevođenja nije tekao tako glatko. Naime, razvoj je popraćen nizom uspona i padova, a upravo je cilj ovog znanstvenog rada dati kritičan i sistematiziran pregled svih ključnih faza razvoja navedene tehnologije, i to u kontekstu svjetskih, ali i domaćih istraživanja u tom području.Automatic machine translation has become a truly irreplaceable part of a large number of organizations that operate in an international environment and in need of generating large amounts of translations for their documentation. Today, machine translation is considered one of the indispensable disruptive technologies that greatly contribute to the complete transformation of business processes in the segment of translating texts written in natural language. The idea behind machine translation is to enable the automation of at least part of the translation process, especially when it comes to a large amount of data, in order to speed up the overall business of an organization and thus gain a competitive advantage in a rapidly changing market, to which one needs to adapt quickly. But the development of automatic machine translation technology did not go so smoothly. Namely, the development is accompanied by a series of ups and downs, and the aim of this very research paper is to give a critical and systematic overview of all key stages of development of this technology, in the context of global and domestic research in this area

Statistical machine translation system and computational domain adaptation

Statističko strojno prevođenje temeljeno na frazama jedan je od mogućih pristupa automatskom strojnom prevođenju. U radu su predložene metode za poboljšanje kvalitete strojnog prijevoda prilagodbom određenih parametara u modelu sustava za statističko strojno prevođenje. Ideja rada bila jest izgraditi sustave za statističko strojno prevođenje temeljeno na frazama za hrvatski i engleski jezik. Sustavi su trenirani za dva jezična smjera, na dvije domene, na paralelnim korpusima različitih veličina i obilježja za hrvatsko-engleski i englesko-hrvatski jezični par, nakon čega proveden postupak ugađanja sustava. Istraženi su hibridni sustavi koji objedinjuju značajke obiju domena. Time je ispitan izravan utjecaj adaptacije domene na kvalitetu automatskog strojnog prijevoda hrvatskog jezika, a nova saznanja mogu koristiti pri izgradnji novih sustava. Provedena je automatska i ljudska evaluacija (vrednovanje) strojnih prijevoda, a dobiveni rezultati uspoređeni su s rezultatima strojnih prijevoda dobivenih primjenom postojećih web servisa za statističko strojno prevođenje.Phrase-based statistical machine translation is one of possible automatic machine translation approaches. This work proposes methods for increasing the quality of machine translation by adapting certain parameters in the statistical machine translation model. The idea was to build phrase-based statistical machine translation systems for Croatian and English language. The systems were be trained for two directions, on two domains, on parallel corpora of different sizes and characteristics for Croatian-English and English-Croatian language pair, after which the tuning procedure was conducted. Afterwards, hybrid systems which combine features of both domains were investigated. Thereby the direct impact of domain adaptation on the quality of automatic machine translation of Croatian language was explored, whereas new findings can be utilised for building new systems. Automatic and human evaluation of machine translations were carried out, while obtained results were compared with results obtained from applying existing statistical machine translation web services

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists