Isin Demirsahin

Isin Demirsahin

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    Criteria for Useful Automatic Romanization in South Asian Languages
    Proceedings of the 13th Language Resources and Evaluation Conference.(LREC), European Language Resources Association (ELRA), 20-25 June, Marseille, France(2022), 6662‑6673
    Preview abstract This paper presents a number of possible criteria for systems that transliterate South Asian languages from their native scripts into the Latin script. This process is also known as romanization. These criteria are related to either fidelity to human linguistic behavior (pronunciation transparency, naturalness and conventionality) or processing utility for people (ease of input) as well as under-the-hood in systems (invertibility and stability across languages and scripts). When addressing these differing criteria several linguistic considerations, such as modeling of prominent phonological processes and their relation to orthography, need to be taken into account. We discuss these key linguistic details in the context of Brahmic scripts and languages that use them, such as Hindi and Malayalam. We then present the core features of several romanization algorithms, implemented in finite state transducer (FST) formalism, that address differing criteria. Implementation of these algorithms will be released as part of the Nisaba finite-state script processing library. View details
    Processing South Asian languages written in the Latin script: the Dakshina dataset
    Lawrence Wolf-Sonkin
    Christo Kirov
    Sabrina J. Mielke
    Keith Hall
    Proceedings of the 12th Conference on Language Resources and Evaluation (LREC)(2020), 2413–2423
    Preview abstract This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet. We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections. We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text. View details
    Open-Source High Quality Speech Datasets for Basque, Catalan and Galician
    Alena Butryna
    Clara E. Rivera
    Proc. of 1st Joint Spoken Language Technologies for Under-Resourced Languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) Workshop (SLTU-CCURL 2020), European Language Resources Association (ELRA), 11--12 May, Marseille, France, pp. 21-27
    Preview abstract This paper introduces three new open speech datasets for Basque, Catalan and Galician, which are languages of Spain, where Catalan is furthermore the official language of the Principality of Andorra. The datasets consist of high-quality multi-speaker recordings of the three languages along with the associated transcriptions. The resulting corpora include over 33 hours of crowd-sourced recordings of 132 male and female native speakers. The recording scripts also include material for elicitation of global and local place names, personal and business names. The datasets are released under a permissive license and are available for free download for commercial, academic and personal use. The high-quality annotated speech datasets described in this paper can be used to, among other things, build text-to-speech systems, serve as adaptation data in automatic speech recognition and provide useful phonetic and phonological insights in corpus linguistics. View details
    Open-source Multi-speaker Corpora of the English Accents in the British Isles
    Clara E. Rivera
    Proc. 12th Language Resources and Evaluation Conference (LREC 2020), European Language Resources Association (ELRA), 11--16 May, Marseille, France, 6532‑-6541
    Preview abstract This paper presents a dataset of transcribed high-quality audio of English sentences recorded by volunteers speaking with different accents of the British Isles. The dataset is intended for linguistic analysis as well as use for speech technologies. The recording scripts were curated specifically for accent elicitation, covering a variety of phonological phenomena and providing a high phoneme coverage. The scripts include pronunciations of global locations, major airlines and common personal names in different accents; and native speaker pronunciations of local words. Overlapping lines for all speakers were included for idiolect elicitation which include the same or similar lines with other existing resources such as the CSTR VCTK corpus and the Speech Accent Archive to allow for easy comparison of personal and regional accents. The resulting corpora include over 31 hours of recordings from 120 volunteers who self-identify as native speakers of Southern England, Midlands, Northern England, Welsh, Scottish and Irish varieties of English. View details
    Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech
    Fei He
    Shan Hui Cathy Chu
    Supheakmungkol Sarin
    Knot Pipatsrisawat
    Alena Butryna
    Proc. 12th Language Resources and Evaluation Conference (LREC 2020), European Language Resources Association (ELRA), 11--16 May, Marseille, France, pp. 6504-6513
    Preview abstract In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish. We first present public speech datasets for Argentinian, Chilean, Colombian, Peruvian, Puerto Rican and Venezuelan Spanish specifically constructed with text-to-speech applications in mind using crowd-sourcing. We then compare the monodialectal voices built with minimal data to a multidialectal model built by pooling all the resources from all dialects. Our results show that the multidialectal model outperforms the monodialectal baseline models. We also experiment with a ``zero-resource'' dialect scenario where we build a multidialectal voice for a dialect while holding out target dialect recordings from the training data. View details
    Developing an Open-Source Corpus of Yoruba Speech
    Clara E. Rivera
    Kólá Túbòsún
    Proc. of Interspeech 2020, International Speech Communication Association (ISCA), October 25--29, Shanghai, China, 2020., pp. 404-408
    Preview abstract This paper introduces an open-source speech dataset for Yoruba - one of the largest low-resource West African languages spoken by at least 22 million people. Yoruba is one of the official languages of Nigeria, Benin and Togo, and is spoken in other neighboring African countries and beyond. The corpus consists of over four hours of 48 kHz recordings from 36 male and female volunteers and the corresponding transcriptions that include disfluency annotation. The transcriptions have full diacritization, which is vital for pronunciation and lexical disambiguation. The annotated speech dataset described in this paper is primarily intended for use in text-to-speech systems, serve as adaptation data in automatic speech recognition and speech-to-speech translation, and provide insights in West African corpus linguistics. We demonstrate the use of this corpus in a simple statistical parametric speech synthesis (SPSS) scenario evaluating it against the related languages from the CMU Wilderness dataset and the Yoruba Lagos-NWU corpus. View details
    Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems
    Fei He
    Shan Hui Cathy Chu
    Clara E. Rivera
    Martin Jansche
    Supheakmungkol Sarin
    Knot Pipatsrisawat
    Proc. 12th Language Resources and Evaluation Conference (LREC 2020), European Language Resources Association (ELRA), 11--16 May, Marseille, France, 6494‑-6503
    Preview abstract We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India. The corpora is primarily intended for use in text-to-speech (TTS) applications, such as constructing multilingual voices or being used for speaker or language adaptation. The data can also be useful for automatic speech recognition (ASR) in various multilingual scenarios. Most of the corpora (apart from Marathi, which is a female-only database) consist of at least 2,000 recorded lines from female and male native speakers of the language. We present the methodological details behind corpora acquisition, which can be scaled to acquiring the data for more languages of interest. We describe the experiments in building a multilingual text-to-speech model that is constructed by combining our corpora. Our results indicate that using these corpora results in good quality voices, with Mean Opinion Scores (MOS) $>$ 3.6, for all the languages tested. We believe that these resources, released with an open-source license, and the described methodology will help developing speech applications for the Indic languages and aid corpora development for other, smaller, languages of India and beyond. View details
    Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An Overview
    Alena Butryna
    Shan Hui Cathy Chu
    Linne Ha
    Fei He
    Martin Jansche
    Chen Fang Li
    Tatiana Merkulova
    Yin May Oo
    Knot Pipatsrisawat
    Clara E. Rivera
    Supheakmungkol Sarin
    Pasindu De Silva
    Keshan Sodimana
    Jaka Aris Eko Wibawa
    2019 UNESCO International Conference Language Technologies for All (LT4All): Enabling Linguistic Diversity and Multilingualism Worldwide, 4--6 December, Paris, France, pp. 91-94
    Preview abstract This paper presents an overview of a program designed to address the growing need for developing free speech resources for under-represented languages. At present we have released 38 datasets for building text-to-speech and automatic speech recognition applications for languages and dialects of South and Southeast Asia, Africa, Europe and South America. The paper describes the methodology used for developing such corpora and presents some of our findings that could benefit under-represented language community. View details
    A Syntactically Expressive Morphological Analyzer for Turkish
    Tolga Kayadelen
    Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing, Association for Computational Linguistics, Dresden, Germany(2019), pp. 65-75
    Preview abstract We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it. The model captures intricacies of Turkish morphology-syntax interface, thus could be used as a baseline that guides language model development. It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations. The morphological analyzer is freely available. It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars. View details
    A Unified Phonological Representation of South Asian Languages for Multilingual Text-to-Speech
    Martin Jansche
    Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU), International Speech Communication Association (ISCA), 29--31 August, Gurugram, India(2018), pp. 80-84
    Preview abstract We present a multilingual phoneme inventory and inclusion mappings from the native inventories of several major South Asian languages for multilingual parametric text-to-speech synthesis (TTS). Our goal is to reduce the need for training data when building new TTS voices by leveraging available data for similar languages within a common feature design. For West Bengali, Gujarati, Kannada, Malayalam, Marathi, Tamil, Telugu, and Urdu we compare TTS voices trained only on monolingual data with voices trained on multilingual data from 12 languages. In subjective evaluations multilingually trained voices outperform (or in a few cases are statistically tied with) the corresponding monolingual voices. The multilingual setup can further be used to synthesize speech for languages not seen in the training data; preliminary evaluations lean towards good. Our results indicate that pooling data from different languages in a single acoustic model can be beneficial, opening up new uses and research questions. View details
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