Machine Assisted Analysis of Vowel Length Contrasts in Wolof

Growing digital archives and improving algorithms for automatic analysis of text and speech create new research opportunities for fundamental research in phonetics. Such empirical approaches allow statistical evaluation of a much larger set of hypothesis about phonetic variation and its conditioning factors (among them geographical / dialectal variants). This paper illustrates this vision and proposes to challenge automatic methods for the analysis of a not easily observable phenomenon: vowel length contrast. We focus on Wolof, an under-resourced language from Sub-Saharan Africa. In particular, we propose multiple features to make a fine evaluation of the degree of length contrast under different factors such as: read vs semi spontaneous speech ; standard vs dialectal Wolof. Our measures made fully automatically on more than 20k vowel tokens show that our proposed features can highlight different degrees of contrast for each vowel considered. We notably show that contrast is weaker in semi-spontaneous speech and in a non standard semi-spontaneous dialect.Comment: Accepted to Interspeech 201

Speed perturbation and vowel duration modeling for ASR in Hausa and Wolof languages

International audienceAutomatic Speech Recognition (ASR) for (under-resourced) Sub-Saharan African languages faces several challenges: small amount of transcribed speech, written language normalization issues, few text resources available for language modeling, as well as specific features (tones, morphology, etc.) that need to be taken into account seriously to optimize ASR performance. This paper tries to address some of the above challenges through the development of ASR systems for two Sub-Saharan African languages: Hausa and Wolof. First, we investigate data augmentation technique (through speed perturbation) to overcome the lack of resources. Secondly, the main contribution is our attempt to model vowel length contrast existing in both languages. For reproducible experiments, the ASR systems developed for Hausa and Wolof are made available to the research community on github. To our knowledge, the Wolof ASR system presented in this paper is the first large vocabulary continuous speech recognition system ever developed for this language

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

Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of Wolof

International audienceThis article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our github repository

Collecte de parole pour l’étude des langues peu dotées ou en danger avec l’application mobile Lig-Aikuma

International audienceNous rapportons dans cet article les travaux en cours portant sur la collecte de langues africaines peu dotées ou en danger. Une collecte de données a été menée à l'aide d'une version modifiée de l'application Android AIKUMA, initialement développée par Steven Bird et coll. (Bird et al., 2014). Les modifications apportées suivent les spécifications du projet franco-allemand ANR/DFG BULB 1 pour faciliter la collecte sur le terrain de corpus de parole parallèles. L'application résultante, appelée LIG-AIKUMA, a été testée avec succès sur plusieurs smartphones et tablettes et propose plusieurs modes de fonctionnement (enregistrement de parole, respeaking de parole, traduction et élicitation). Entre autres fonctionnalités, LIG-AIKUMA permet la génération et la manipulation avancée de fichiers de métadonnées ainsi que la prise en compte d'informations d'alignement entre phrases prononcées parallèles dans les modes de respeaking et de traduction. L'application a été utilisée aux cours de campagnes de collecte sur le terrain, au Congo-Brazzaville, permettant l'acquisition de 80 heures de parole. La conception de l'application et l'illustration de son usage dans deux campagnes de collecte sont décrites plus en détail dans cet article

Parallel Speech Collection for Under-resourced Language Studies Using the Lig-Aikuma Mobile Device App

International audienceThis paper reports on our ongoing efforts to collect speech data in under-resourced or endangered languages of Africa. Data collection is carried out using an improved version of the Android application Aikuma developed by Steven Bird and colleagues 1. Features were added to the app in order to facilitate the collection of parallel speech data in line with the requirements of the French-German ANR/DFG BULB (Breaking the Unwritten Language Barrier) project. The resulting app, called Lig-Aikuma, runs on various mobile phones and tablets and proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). Lig-Aikuma's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping. It was used for field data collections in Congo-Brazzaville resulting in a total of over 80 hours of speech. Design issues of the mobile app as well as the use of Lig-Aikuma during two recording campaigns, are further described in this paper

LIG-AIKUMA: a Mobile App to Collect Parallel Speech for Under-Resourced Language Studies

International audienceThis paper reports on our ongoing efforts to collect speech data in under-resourced or endangered languages of Africa. Data collection is carried out using an improved version of the An-droid application (AIKUMA) developed by Steven Bird and colleagues [1]. Features were added to the app in order to facilitate the collection of parallel speech data in line with the requirements of the French-German ANR/DFG BULB (Breaking the Unwritten Language Barrier) project. The resulting app, called LIG-AIKUMA, runs on various mobile phones and tablets and proposes a range of different speech collection modes (recording , respeaking, translation and elicitation). It was used for field data collections in Congo-Brazzaville resulting in a total of over 80 hours of speech

Speech Technologies for African Languages: Example of a Multilingual Calculator for Education

International audienceThis paper presents our achievements after 18 months of the ALFFA project dealing with African languages technologies. We focus on a multilingual calculator (Android app) that will be demonstrated during the Show and Tell session

Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping

Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping