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

LESSONS LEARNED AFTER DEVELOPMENT AND USE OF A DATA COLLECTION APP FOR LANGUAGE DOCUMENTATION (LIG-AIKUMA)

International audienceLig-Aikuma is a free Android app running on various mobile phones and tablets. It proposes a range of different speech collection modes (recording, respeaking, translation and elicitation) and offers the possibility to share recordings between users. More than 250 hours of speech in 6 different languages from sub-Saharan Africa (including 3 oral languages in the process of being documented) have already been collected with Lig-Aikuma. This paper presents the lessons learned after 3 years of development and use of Lig-Aikuma. While significant data collections were conducted, this has not been done without difficulties. Some mixed results lead us to stress the importance of design choices, data sharing architecture and user manual. We also discuss other potential uses of the app, discovered during its deployment: data collection for language revitalisation, data collection for speech technology development (ASR) and enrichment of existing corpora through the addition of spoken comments

A small Griko-Italian speech translation corpus

This paper presents an extension to a very low-resource parallel corpus collected in an endangered language, Griko, making it useful for computational research. The corpus consists of 330 utterances (about 2 hours of speech) which have been transcribed and translated in Italian, with annotations for word-level speech-to-transcription and speech-to-translation alignments. The corpus also includes morpho syntactic tags and word-level glosses. Applying an automatic unit discovery method, pseudo-phones were also generated. We detail how the corpus was collected, cleaned and processed, and we illustrate its use on zero-resource tasks by presenting some baseline results for the task of speech-to-translation alignment and unsupervised word discovery. The dataset will be available online, aiming to encourage replicability and diversity in computational language documentation experiments

Contextualising Levels of Language Resourcedness affecting Digital Processing of Text

Application domains such as digital humanities and tool like chatbots involve some form of processing natural language, from digitising hardcopies to speech generation. The language of the content is typically characterised as either a low resource language (LRL) or high resource language (HRL), also known as resource-scarce and well-resourced languages, respectively. African languages have been characterized as resource-scarce languages (Bosch et al. 2007; Pretorius & Bosch 2003; Keet & Khumalo 2014) and English is by far the most well-resourced language. Varied language resources are used to develop software systems for these languages to accomplish a wide range of tasks. In this paper we argue that the dichotomous typology LRL and HRL for all languages is problematic. Through a clear understanding of language resources situated in a society, a matrix is developed that characterizes languages as Very LRL, LRL, RL, HRL and Very HRL. The characterization is based on the typology of contextual features for each category, rather than counting tools, and motivation is provided for each feature and each characterization. The contextualisation of resourcedness, with a focus on African languages in this paper, and an increased understanding of where on the scale the language used in a project is, may assist in, among others, better planning of research and implementation projects. We thus argue in this paper that the characterization of language resources within a given scale in a project is an indispensable component particularly in the context of low-resourced languages

Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)

International audienceThis article presents multimodal and parallel data collections in Mboshi, as part of the French-German BULB project. It aims at supporting documentation and providing digital resources for less resourced languages with the help of speech and language-based technology. The data collection specifications thus have to meet both field linguists' and computer scientists' requirements, which are large corpora for the latter and linguistically dense data for the former. Beyond speech, the collection comprises pictures and videos documenting social practices, agriculture, wildlife and plants. Visual supports aimed at encouraging people to comment on objects which are meaningful in their daily lives. Speech recordings are composed of the original speech in Mboshi, a respoken version and a translated version to French. These three speech streams remain time-aligned thanks to LIG-AIKUMA, which adds new features to a previous AIKUMA application. The speech corpus includes read material (5k sentences, Bible), verb conjugations and a large part of spontaneous speech (conversations, picture descriptions) resulting in over 50 hours of Mboshi speech, of which 20 hours are already respoken and orally translated to French. These parallel oral data are intended for linguistic documentation (tonology, phonology...) and automatic processing (corpus annotation, alignment between Mboshi speech and French translations)