Advances in speech and language processing have enabled the creation of applications that could, in principle, accelerate the process of language documentation, as speech communities and linguists work on urgent language documentation and reclamation projects. However, such systems have yet to make a significant impact on language documentation, as resource requirements limit the broad applicability of these new techniques. We aim to exploit the framework of shared tasks to focus the technology research community on tasks which address key pain points in language documentation. Here we present initial steps in the implementation of these new shared tasks, through the creation of data sets drawn from endangered language repositories and baseline systems to perform segmentation and speaker labeling of these audio recordings—important enabling steps in the documentation process. This paper motivates these tasks with a use case, describes data set curation and baseline systems, and presents results on this data. We then highlight the challenges and ethical considerations in developing these speech processing tools and tasks to support endangered language documentation.

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Developing a Shared Task for Speech Processing on Endangered Languages

Speaker Recognition is the collective name of problems given to identifying a person or a set of persons using his/her voice. Variation of speaker speaking styles due to different languages can make speaker recognition a difficult task. In this paper, the main aim was to develop a system and compare different efficient text-independent Bengali speaker recognition systems that can give good rates of accuracy (greater than 90%) with not more than 10 minutes of speech data available for each speaker and can easily produce results without long amounts of delay. The experiments were carried out using the SHRUTI Bengali speech database and validated using TED-EX database. We have also analyzed different features of a Bengali speaker using GMM-UBM framework, Joint Factor Analysis, i-vectors, CNN and RNN. Elaborate comparisons and classifications are carried out based on training durations and languages spoken by the speakers.

Potsangbam Madhubala

Papers

Preliminary Acoustic Analysis of Manipuri Vowels.