Beyond English text: Multilingual and multimedia information retrieval.

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Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Keyword spotting for audiovisual archival search in Uralic languages

Publisher Copyright: © 2021 IWCLUL 2021 - 7th International Workshop on Computational Linguistics of Uralic Languages, Proceedings. All rights reserved.In this study we investigate the potential of using Automatic Speech Recognition (ASR) for keyword spotting for four Uralic languages: Finnish, Hungarian, Estonian and Komi. These languages also represent different levels on the high and low resource continuum. Although the accuracy of the ASR systems show there is a long way to go, we show that they still have potential to be useful for downstream tasks such as keyword spotting. By using a simple text search after running ASR, we are already able to achieve an F1 score of between 0.15 and 0.33, a precision of nearly 0.90 for Estonian and Hungarian, and a precision of 0.76 for Komi.Peer reviewe

Advances in unlimited-vocabulary speech recognition for morphologically rich languages

Automatic speech recognition systems are devices or computer programs that convert human speech into text or make actions based on what is said to the system. Typical applications include dictation, automatic transcription of large audio or video databases, speech-controlled user interfaces, and automated telephone services, for example. If the recognition system is not limited to a certain topic and vocabulary, covering the words in the target languages as well as possible while maintaining a high recognition accuracy becomes an issue. The conventional way to model the target language, especially in English recognition systems, is to limit the recognition to the most common words of the language. A vocabulary of 60 000 words is usually enough to cover the language adequately for arbitrary topics. On the other hand, in morphologically rich languages, such as Finnish, Estonian and Turkish, long words can be formed by inflecting and compounding, which makes it difficult to cover the language adequately by vocabulary-based approaches. This thesis deals with methods that can be used to build efficient speech recognition systems for morphologically rich languages. Before training the statistical n-gram language models on a large text corpus, the words in the corpus are automatically segmented into smaller fragments, referred to as morphs. The morphs are then used as modelling units of the n-gram models instead of whole words. This makes it possible to train the model on the whole text corpus without limiting the vocabulary and enables the model to create even unseen words by joining morphs together. Since the segmentation algorithm is unsupervised and data-driven, it can be readily used for many languages. Speech recognition experiments are made on various Finnish recognition tasks and some of the experiments are also repeated on an Estonian task. It is shown that the morph-based language models reduce recognition errors when compared to word-based models. It seems to be important, however, that the n-gram models are allowed to use long morph contexts, especially if the morphs used by the model are short. This can be achieved by using growing and pruning algorithms to train variable-length n-gram models. The thesis also presents data structures that can be used for representing the variable-length n-gram models efficiently in recognition systems. By analysing the recognition errors made by Finnish recognition systems it is found out that speaker adaptive training and discriminative training methods help to reduce errors in different situations. The errors are also analysed according to word frequencies and manually defined error classes

The automatic analysis of classroom talk

The SMART SPEECH Project is a joint venture between three Finnish universities and a Chilean university. The aim is to develop a mobile application that can be used to record classroom talk and enable observations to be made of classroom interactions. We recorded Finnish and Chilean physics teachers’ speech using both a conventional microphone/dictator setup and a microphone/mobile application setup. The recordings were analysed via automatic speech recognition (ASR). The average word error rate achieved for the Finnish teachers’ speech was under 40%. The ASR approach also enabled us to determine the key topics discussed within the Finnish physics lessons under scrutiny. The results here were promising as the recognition accuracy rate was about 85% on average

On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Transforming Archived Resources with Language Technology : From Manuscripts to Language Documentation

Publisher Copyright: © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)Transcriptions in different languages are a ubiquitous data format in linguistics and in many other fields in the humanities. However, the majority of these resources remain both under-used and under-studied. This may be the case even when the materials have been published in print, but is certainly the case for the majority of unpublished transcriptions. Our paper presents a workflow adapted in the research project Language Documentation Meets Language Technology, which combines text recognition, automatic transliteration and forced alignment into a process which allows us to convert earlier transcribed documents to a structure that is comparable with contemporary language documentation corpora. This has complex practical and methodological considerations.Peer reviewe

Transforming Archived Resources with Language Technology : From Manuscripts to Language Documentation

Publisher Copyright: © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)Transcriptions in different languages are a ubiquitous data format in linguistics and in many other fields in the humanities. However, the majority of these resources remain both under-used and under-studied. This may be the case even when the materials have been published in print, but is certainly the case for the majority of unpublished transcriptions. Our paper presents a workflow adapted in the research project Language Documentation Meets Language Technology, which combines text recognition, automatic transliteration and forced alignment into a process which allows us to convert earlier transcribed documents to a structure that is comparable with contemporary language documentation corpora. This has complex practical and methodological considerations.Peer reviewe

The role of linguistics in language teaching: the case of two, less widely taught languages - Finnish and Hungarian

This paper discusses the role of various linguistic sub-disciplines in teaching Finnish and Hungarian. We explain the status of Finnish and Hungarian at University College London and in the UK, and present the principle difficulties in learning and teaching these two languages. We also introduce our courses and student profiles. With the support of examples from our own teaching, we argue that a linguistically oriented approach is well suited for less widely used and less taught languages as it enables students to draw comparative and historical parallels, question terminologies and raise their sociolinguistic and pragmatic awareness. A linguistic approach also provides students with skills for further language learning