152 research outputs found
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
Amharic Speech Recognition for Speech Translation
International audienceThe state-of-the-art speech translation can be seen as a cascade of Automatic Speech Recognition, Statistical Machine Translation and Text-To-Speech synthesis. In this study an attempt is made to experiment on Amharic speech recognition for Amharic-English speech translation in tourism domain. Since there is no Amharic speech corpus, we developed a read-speech corpus of 7.43hr in tourism domain. The Amharic speech corpus has been recorded after translating standard Basic Traveler Expression Corpus (BTEC) under a normal working environment. In our ASR experiments phoneme and syllable units are used for acoustic models, while morpheme and word are used for language models. Encouraging ASR results are achieved using morpheme-based language models and phoneme-based acoustic models with a recognition accuracy result of 89.1%, 80.9%, 80.6%, and 49.3% at character, morph, word and sentence level respectively. We are now working towards designing Amharic-English speech translation through cascading components under different error correction algorithms
Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection
While there has been significant progress in ASR, African-accented clinical
ASR has been understudied due to a lack of training datasets. Building robust
ASR systems in this domain requires large amounts of annotated or labeled data,
for a wide variety of linguistically and morphologically rich accents, which
are expensive to create. Our study aims to address this problem by reducing
annotation expenses through informative uncertainty-based data selection. We
show that incorporating epistemic uncertainty into our adaptation rounds
outperforms several baseline results, established using state-of-the-art (SOTA)
ASR models, while reducing the required amount of labeled data, and hence
reducing annotation costs. Our approach also improves out-of-distribution
generalization for very low-resource accents, demonstrating the viability of
our approach for building generalizable ASR models in the context of accented
African clinical ASR, where training datasets are predominantly scarce
Investigating data sharing in speech recognition for an underresourced language: the case of algerian dialect
International audienceThe Arabic language has many varieties, including its standard form, Modern Standard Arabic (MSA), and its spoken forms, namely the dialects. Those dialects are representative examples of under-resourced languages for which automatic speech recognition is considered as an unresolved issue. To address this issue, we recorded several hours of spoken Algerian dialect and used them to train a baseline model. This model was boosted afterwards by taking advantage of other languages that impact this dialect by integrating their data in one large corpus and by investigating three approaches: multilingual training, multitask learning and transfer learning. The best performance was achieved using a limited and balanced amount of acoustic data from each additional language, as compared to the data size of the studied dialect. This approach led to an improvement of 3.8% in terms of word error rate in comparison to the baseline system trained only on the dialect data
UmobiTalk: Ubiquitous Mobile Speech Based Learning Language Translator for Sesotho Language
Published ThesisThe need to conserve the under-resourced languages is becoming more urgent as some of them are becoming extinct; natural language processing can be used to redress this. Currently, most initiatives around language processing technologies are focusing on western languages such as English and French, yet resources for such languages are already available. The Sesotho language is one of the under-resourced Bantu languages; it is mostly spoken in Free State province of South Africa and in Lesotho. Like other parts of South Africa, Free State has experienced high number of migrants and non-Sesotho speakers from neighboring provinces and countries; such people are faced with serious language barrier problems especially in the informal settlements where everyone tends to speak only Sesotho. Non-Sesotho speakers refers to the racial groups such as Xhosas, Zulus, Coloureds, Whites and more, in which Sesotho language is not their native language.
As a solution to this, we developed a parallel corpus that has English as source and Sesotho as a target language and packaged it in UmobiTalk - Ubiquitous mobile speech based learning translator. UmobiTalk is a mobile-based tool for learning Sesotho for English speakers. The development of this tool was based on the combination of automatic speech recognition, machine translation and speech synthesis
- …