Construction faiblement supervisée d'un phonétiseur pour la langue Iban à partir de ressources en Malais

International audienceThis paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to bootstrap automatic speech recognition (ASR) for a very under-resourced language : iban (also spoken in Malaysia on the Borneo Island part). Resources in iban for building a speech recognition were nonexistent. For this, we tried to take advantage of a language from the same family with several similarities. First, to deal with the pronunciation dictionary, we proposed a bootstrapping strategy to develop an iban pronunciation lexicon from a Malay one. A hybrid version, mix of Malay and iban pronunciations, was also built and evaluated. Following this, we experimented with three iban ASRs ; each depended on either one of the three different pronunciation dictionaries : Malay, iban or hybrid.Cet article décrit notre collecte de ressources pour la langue iban (parlée notamment sur l'île de Bornéo), dans l'objectif de construire un système de reconnaissance automatique de la parole pour cette langue. Nous nous sommes plus particulièrement focalisés sur une méthodologie d'amorçage du lexique phonétisé à partir d'une langue proche (le malais). Les performances des premiers systèmes de reconnaissance automatique de la parole construits pour l'iban (< 20% WER) montrent que l'utilisation d'un phonétiseur déjà disponible dans une langue proche (le malais) est une option tout à fait viable pour amorcer le développement d'un système de RAP dans une nouvelle langue très peu dotée. Une première analyse des erreurs fait ressortir des problèmes bien connus pour les langues peu dotées : problèmes de normalisation de l'orthographe, erreurs liées à la morphologie (séparation ou non des affixes de la racine)

USING MALAY RESOURCES TO BOOTSTRAP ASR FOR A VERY UNDER-RESOURCED LANGUAGE: IBAN

International audienceThis paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to boot- strap automatic speech recognition (ASR) for a very under- resourced language: Iban (also spoken in Malaysia on the Borneo Island part). Resources in Iban for building a speech recognition were nonexistent. For this, we tried to take ad- vantage of a language from the same family with several similarities. First, to deal with the pronunciation dictionary, we proposed a bootstrapping strategy to develop an Iban pronunciation lexicon from a Malay one. A hybrid version, mix of Malay and Iban pronunciations, was also built and evaluated. Following this, we experimented with three Iban ASRs; each depended on either one of the three different pronunciation dictionaries: Malay, Iban or hybrid

Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban

International audienceThis paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech

Using closely-related language to build an ASR for a very under-resourced language: Iban

International audienceThis paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, Iban, a language that is mainly spoken in Sarawak, Malaysia. We collected 8 hours of data to begin this study due to no resources for ASR exist. We employed bootstrapping techniques involving a closely-related language for rapidly building and improve an Iban system. First, we used already available data from Malay, a local dominant language in Malaysia, to bootstrap grapheme-to-phoneme system (G2P) for the target language. We also built various types of G2Ps, including a grapheme-based and an English G2P, to produce different versions of dictionaries. We tested all of the dictionaries on the Iban ASR to provide us the best version. Second, we improved the baseline GMM system word error rate (WER) result by utilizing subspace Gaussian mixture models (SGMM). To test, we set two levels of data sparseness on Iban data; 7 hours and 1 hour transcribed speech. We investigated cross-lingual SGMM where the shared parameters were obtained either in monolingual or multilingual fashion and then applied to the target language for training. Experiments on out-of-language data, English and Malay, as source languages result in lower WERs when Iban data is very limited

Creating Interactive Videos for Teaching and Learning

Due to the COVID-19 pandemic outbreak, Malaysia began its lockdown on 18 March 2020, after our Prime Minister Yang Amat Berhormat Tan Sri Dato’ Haji Muhyiddin bin Haji Mohd Yassin officially announced the Movement Control Order (MCO) two days prior. At such short notice, UNIMAS took the drastic decision to embrace fully online teaching and learning (T&L) for all courses. We had to revamp our teaching and learning strategy with the most notable shift; recorded lecture videos. In a physical lecture, we can directly engage with students, however, with videos, such interaction is lost. How do we know if the students are actually paying attention or, do they understand what is presented in the video? In this article, we share our experience in using one of the hidden gems in eLEAP that is very useful for creating interactive videos. At the end of the article, we provide six simple steps that can be used as a guide

Mobile Application for Improving Speech and Text Data Collection Approach

This paper describes our work in developing a mobile application for collecting language speech and text data. The application is built to assist linguists or researchers in simplifying their tasks in data collection who of native speakers living in remote interiors. Researchers rely on numerous apparatus to carry out their tasks to capture audio or text from far to reach places, but with this mobile application, they would only need to carry one device, which can ease their logistics troubles. The mobile app, named as Kalaka, is designed for users to store details of native speakers, record speech and insert speech transcripts all in one platform. Kalaka is built on the Android platform, which allows data stored in the mobile device to be transferred to a cloud storage using WiFi networks. Usability tests performed in respondents shows, all participants in the evaluation are able to use the application to record their voices and save texts. We also received positive feedbacks on the mobile application from our survey, with more than half of the respondents gave their confidence using Kalaka and they would use the system frequently

Semi-supervised G2P Bootstrapping and Its Application to ASR for a Very Under-resourced Language: Iban

This paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to bootstrap automatic speech recognition (ASR) for a very under-resourced language: Iban (also spoken in Malaysia on the Borneo Island part). Resources in Iban for building a speech recognition were nonexistent. For this, we tried to take advantage of a language from the same family with several similarities. First, to deal with the pronunciation dictionary, we proposed a bootstrapping strategy to develop an Iban pronunciation lexicon from a Malay one. A hybrid version, mix of Malay and Iban pronunciations, was also built and evaluated. Following this, we experimented with three Iban ASRs; each depended on either one of the three different pronunciation dictionaries: Malay, Iban or hybrid