Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project

Czech is a very specific language due to its large differences between the formal and the colloquial form of speech. While the formal (written) form is used mainly in official documents, literature, and public speeches, the colloquial (spoken) form is used widely among people in casual speeches. This gap introduces serious problems for ASR systems, especially when training or evaluating ASR models on datasets containing a lot of colloquial speech, such as the MALACH project. In this paper, we are addressing this problem in the light of a new paradigm in end-to-end ASR systems -- recently introduced self-supervised audio Transformers. Specifically, we are investigating the influence of colloquial speech on the performance of Wav2Vec 2.0 models and their ability to transcribe colloquial speech directly into formal transcripts. We are presenting results with both formal and colloquial forms in the training transcripts, language models, and evaluation transcripts.Comment: to be published in Proceedings of TSD 202

System for fast lexical and phonetic spoken term detection in a czech cultural heritage archive,”

Abstract The main objective of the work presented in this paper was to develop a complete system that would accomplish the original visions of the MALACH project. Those goals were to employ automatic speech recognition and information retrieval techniques to provide improved access to the large video archive containing recorded testimonies of the Holocaust survivors. The system has been so far developed for the Czech part of the archive only. It takes advantage of the state-of-the art speech recognition system tailored to the challenging properties of the recordings in the archive (elderly speakers, spontaneous speech, emotionally loaded content) and its close coupling with the actual search engine. The design of the algorithm adopting the spoken term detection approach is focused on the speed of the retrieval. The resulting system is able to search through the 1,000 hours of video constituting the Czech portion of the archive and find query word occurrences in the matter of seconds. The phonetic search implemented alongside the search based on the lexicon words allows to find even the words outside the ASR system lexicon such as names, geographic locations or Jewish slang

Automatsko raspoznavanje hrvatskoga govora velikoga vokabulara

This paper presents procedures used for development of a Croatian large vocabulary automatic speech recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous speech recognition. Presented experiments and results show that the proposed approach for automatic speech recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efficient Croatian large vocabulary speech recognition with word error rates below 5%.Članak prikazuje postupke akustičkog i jezičnog modeliranja sustava za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara. Predloženi akustički modeli su zasnovani na kontekstno-ovisnim skrivenim Markovljevim modelima trifona i hrvatskim fonetskim pravilima. Na hrvatskome govoru prikupljenom u korpusu su ocjenjeni i uspoređeni različiti akustički i jezični modeli. U članku su uspoređ eni i predloženi postupci za izračun vektora značajki za akustičko modeliranje kao i sam pristup akustičkome modeliranju hrvatskoga govora s kojim je postignuta najmanja mjera pogrešno raspoznatih riječi. Predstavljeni su rezultati raspoznavanja spontanog hrvatskog govora neovisni o govorniku. Postignuti rezultati eksperimenata s mjerom pogreške ispod 5% ukazuju na primjerenost predloženih postupaka za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara pomoću vezanih kontekstnoovisnih akustičkih modela na osnovu hrvatskih fonetskih pravila

Методика выбора фонемного набора для автоматического распознавания русской речи

In the paper, selection of best phoneme set for Russian automatic speech recognition is described. For the acoustic modeling, we describe a method based on combination of knowledge-based and statistical approaches to create several different phoneme sets. Applying this method to the Russian phonetic set of the IPA (International Phonetic Alphabet) alphabet, we first reduced it to 47 phonological units and derived several other phoneme sets with different number of phonological units from 27 till 47. Speech recognition experiments using these sets showed that reduced phoneme sets are better for phoneme recognition task and as good for word level speech recognition. For experiment with extra-large vocabulary, we used syntactico-statistical language model, which allowed us to achieve the word recognition accuracy of 73.1%. The results correspond to continuous Russian speech recognition quality obtained by other organizations up to date.В статье описывается выбор оптимального фонемного набора для системы автоматического распознавания русской речи. При создании акустических моделей был предложен комбинированный метод для выбора наилучшего фонемного набора, объединяющий статистическую информацию и фонетические знания. В результате применения данного метода к русскому фонетическому набору алфавита IPA (International Phonetic Alphabet) был получен набор из 47 фонологических единиц, который был преобразован в несколько фонемных наборов с разным размером от 27 до 47 единиц. Эксперименты по распознаванию речи показали, что использование сокращенных фонемных наборов позволяет увеличить точность распознавания фонем. В ходе экспериментов с применением расширенной языковой модели и сверхбольшим словарем точность распознавания слов составила 73,1%. Полученные результаты соответствуют качеству распознавания слитной русской речи, полученному на настоящий момент другими организациями

Adaptation of speech recognition systems to selected real-world deployment conditions

Tato habilitační práce se zabývá problematikou adaptace systémů rozpoznávání řeči na vybrané reálné podmínky nasazení. Je koncipována jako sborník celkem dvanácti článků, které se touto problematikou zabývají. Jde o publikace, jejichž jsem hlavním autorem nebo spoluatorem, a které vznikly v rámci několika navazujících výzkumných projektů. Na řešení těchto projektů jsem se podílel jak v roli člena výzkumného týmu, tak i v roli řešitele nebo spoluřešitele. Publikace zařazené do tohoto sborníku lze rozdělit podle tématu do tří hlavních skupin. Jejich společným jmenovatelem je snaha přizpůsobit daný rozpoznávací systém novým podmínkám či konkrétnímu faktoru, který významným způsobem ovlivňuje jeho funkci či přesnost. První skupina článků se zabývá úlohou neřízené adaptace na mluvčího, kdy systém přizpůsobuje svoje parametry specifickým hlasovým charakteristikám dané mluvící osoby. Druhá část práce se pak věnuje problematice identifikace neřečových událostí na vstupu do systému a související úloze rozpoznávání řeči s hlukem (a zejména hudbou) na pozadí. Konečně třetí část práce se zabývá přístupy, které umožňují přepis audio signálu obsahujícího promluvy ve více než v jednom jazyce. Jde o metody adaptace existujícího rozpoznávacího systému na nový jazyk a metody identifikace jazyka z audio signálu. Obě zmíněné identifikační úlohy jsou přitom vyšetřovány zejména v náročném a méně probádaném režimu zpracování po jednotlivých rámcích vstupního signálu, který je jako jediný vhodný pro on-line nasazení, např. pro streamovaná data.This habilitation thesis deals with adaptation of automatic speech recognition (ASR) systems to selected real-world deployment conditions. It is presented in the form of a collection of twelve articles dealing with this task; I am the main author or a co-author of these articles. They were published during my work on several consecutive research projects. I have participated in the solution of them as a member of the research team as well as the investigator or a co-investigator. These articles can be divided into three main groups according to their topics. They have in common the effort to adapt a particular ASR system to a specific factor or deployment condition that affects its function or accuracy. The first group of articles is focused on an unsupervised speaker adaptation task, where the ASR system adapts its parameters to the specific voice characteristics of one particular speaker. The second part deals with a) methods allowing the system to identify non-speech events on the input, and b) the related task of recognition of speech with non-speech events, particularly music, in the background. Finally, the third part is devoted to the methods that allow the transcription of an audio signal containing multilingual utterances. It includes a) approaches for adapting the existing recognition system to a new language and b) methods for identification of the language from the audio signal. The two mentioned identification tasks are in particular investigated under the demanding and less explored frame-wise scenario, which is the only one suitable for processing of on-line data streams

Automatic processing of computer-transcribed spoken documents from multimedia archives

Tato práce se zaměřuje na řešení komplexního problému jak strukturalizovat (vhodně rozčlenit, textově i foneticky analyzovat a následně upravit) výstup systému pro automatické rozpoznávání řeči tak, aby byl co nejčitelnější pro člověka a zároveň připravený pro efektivní strojové zpracování a vyhledávání. Motivací pro řešení tohoto problému byl výzkumný projekt podporovaný Ministerstvem kultury ČR, jehož cílem bylo přepsat mluvené dokumenty z archivu Českého a Československého rozhlasu a zpřístupnit je pro vyhledávání. Vzhledem k rozsahu archivu (213.000 dokumentů z období 1923 až 2014) bylo nutné navrhnout a zrealizovat takový postup a technologie, které by byly schopny zvládnout nejen obrovské množství dat, ale také specifické problémy související s různou kvalitou záznamů, s přítomností českého i slovenského jazyka v dokumentech, se střídajícími se mluvčími, s prokládáním řeči znělkami, hudebními předěly a písničkami či s hluky na pozadí řeči.This thesis focuses on solving a complex task how to structure (i.e. appropriately divide, textually and phonetically analyze and subsequently modify) the output of the speech recognition system so it is most readable for human and also prepared for effective machine processing and search. Motivation to solve this task was the research project supported by the Czech Ministry of culture, aimed at transcription of spoken documents contained in the Czech and Czechoslovak radio and to make them available for search. Taking into account the archive size (213,000 documents form the years 1923-2014) it was essential to propose and implement such technologies, that were able to handle not only the waste amount of the data but also some specific issues associated with different acoustic quality of the documents, speaker changes, presence of jingles, music divides and song between the speech segments or with background noise

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

CLARIN. The infrastructure for language resources

CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)