Statistical Language Models for Croatian Weather-domain Corpus

Statistical language modelling estimates the regularities in natural languages. Language models are used in speech recognition, machine translation and other applications for speech and language technologies. In this paper we will present a procedure for language models building for the Croatian weather domain corpus. Different types of n-gram statistic language models and smoothing methods for language modelling are presented. Those models are compared in terms of their estimated perplexity

A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments

Most speech and language technologies are trained with massive amounts of speech and text information. However, most of the world languages do not have such resources or stable orthography. Systems constructed under these almost zero resource conditions are not only promising for speech technology but also for computational language documentation. The goal of computational language documentation is to help field linguists to (semi-)automatically analyze and annotate audio recordings of endangered and unwritten languages. Example tasks are automatic phoneme discovery or lexicon discovery from the speech signal. This paper presents a speech corpus collected during a realistic language documentation process. It is made up of 5k speech utterances in Mboshi (Bantu C25) aligned to French text translations. Speech transcriptions are also made available: they correspond to a non-standard graphemic form close to the language phonology. We present how the data was collected, cleaned and processed and we illustrate its use through a zero-resource task: spoken term discovery. The dataset is made available to the community for reproducible computational language documentation experiments and their evaluation.Comment: accepted to LREC 201

Special issue on applications of speech and language technologies in healthcare

In recent years, the exploration and uptake of digital health technologies have advanced rapidly with a real potential impact to revolutionise healthcare delivery and associated industries [...

ASLP-MULAN: Audio speech and language processing for multimedia analytics

Our intention is generating the right mixture of audio, speech and language technologies with big data ones. Some audio, speech and language automatic technologies are available or gaining enough degree of maturity as to be able to help to this objective: automatic speech transcription, query by spoken example, spoken information retrieval, natural language processing, unstructured multimedia contents transcription and description, multimedia files summarization, spoken emotion detection and sentiment analysis, speech and text understanding, etc. They seem to be worthwhile to be joined and put at work on automatically captured data streams coming from several sources of information like YouTube, Facebook, Twitter, online newspapers, web search engines, etc. to automatically generate reports that include both scientific based scores and subjective but relevant summarized statements on the tendency analysis and the perceived satisfaction of a product, a company or another entity by the general population

Models of the Serbian language and their application in speech and language technologies

Statistički jezički model, u teoriji, predstavlja raspodelu verovatnoća nad skupom svih mogućih sekvenci reči nekog jezika. U praksi, to je mehanizam kojim se estimiraju verovatnoće sekvenci, koje su od interesa. Matematički aparat vezan za modele jezika je uglavnom nezavisan od jezika. Međutim, kvalitet obučenih modela ne zavisi samo od algoritama obuke, već prvenstveno od količine i kvaliteta podataka koji su na raspolaganju za obuku. Za jezike sa kompleksnom morfologijom, kao što je srpski, tekstualni korpus za obuku modela mora biti daleko obimniji od korpusa koji bi se koristio kod nekog od jezika sa relativno jednostavnom morfologijom, poput engleskog. Ovo istraživanje obuhvata razvoj jezičkih modela za srpski jezik, počevši od prikupljanja i inicijalne obrade tekstualnih sadržaja, preko adaptacije algoritama i razvoja metoda za rešavanje problema nedovoljne količine podataka za obuku, pa do prilagođavanja i primene modela u različitim tehnologijama, kao što su sinteza govora na osnovu teksta, automatsko prepoznavanje govora, automatska detekcija i korekcija gramatičkih i semantičkih grešaka u tekstovima, a postavljaju se i osnove za primenu jezičkih modela u automatskoj klasifikaciji dokumenata i drugim tehnologijama. Jezgro razvoja jezičkih modela za srpski predstavlja definisanje morfoloških klasa reči na osnovu informacija koje su sadržane u morfološkom rečniku, koji je nastao kao rezultat jednog od ranijih istraživanja.A statistical language model, in theory, represents a probability distribution over sequences of words of a language. In practice, it is a tool for estimating probabilities of word sequences of interest. Mathematical basis related to language models is mostly language independent. However, the quality of trained models depends not only on training algorithms, but on the amount and quality of available training data as well. For languages with complex morphology, such as Serbian, textual corpora for training language models need to be significantly larger than the corpora needed for training language models for languages with relatively simple morphology, such as English. This research represents the entire process of developing language models for Serbian, starting with collecting and preprocessing of textual contents, extending to adaptation of algorithms and development of methods for addressing the problem of insufficient training data, and finally to adaptation and application of the models in different technologies, such as text-to-speech synthesis, automatic speech recognition, automatic detection and correction of grammar and semantic errors in texts, and determining basics for the application of the models in automatic document classification and other tasks. The core of the development of language models for Serbian is defining morphologic classes of words, based on the information contained within the morphologic dictionary of Serbian, which was one of the results of a previous research

NLP for Language Varieties of Italy: Challenges and the Path Forward

Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expression, and history of its speakers. However, over 30 language varieties in Italy are at risk of disappearing within few generations. Language technology has a main role in preserving endangered languages, but it currently struggles with such varieties as they are under-resourced and mostly lack standardized orthography, being mainly used in spoken settings. In this paper, we introduce the linguistic context of Italy and discuss challenges facing the development of NLP technologies for Italy's language varieties. We provide potential directions and advocate for a shift in the paradigm from machine-centric to speaker-centric NLP. Finally, we propose building a local community towards responsible, participatory development of speech and language technologies for languages and dialects of Italy.Comment: 16 pages, 3 figures, 4 table