Enhancing ASR Systems for Under-Resourced Languages through a Novel Unsupervised Acoustic Model Training Technique

• Main Authors: CUCU, H., BUZO, A., BESACIER, L., BURILEANU, C.
• Format: Article
• Language: English
• Published: Stefan cel Mare University of Suceava 2015-02-01
• Series: Advances in Electrical and Computer Engineering
• Subjects: speech recognition; under-resourced languages; unsupervised acoustic modeling; unsupervised training
• Online Access: http://dx.doi.org/10.4316/AECE.2015.01009

Statistical speech and language processing techniques, requiring large amounts of training data, are currently state-of-the-art in automatic speech recognition. For high-resourced, international languages this data is widely available, while for under-resourced languages the lack of data poses serious problems. Unsupervised acoustic modeling can offer a cost and time effective way of creating a solid acoustic model for any under-resourced language. This study describes a novel unsupervised acoustic model training method and evaluates it on speech data in an under-resourced language: Romanian. The key novel factor of the method is the usage of two complementary seed ASR systems to produce high quality transcriptions, with a Character Error Rate (ChER) < 5%, for initially untranscribed speech data. The methodology leads to a relative Word Error Rate (WER) improvement of more than 10% when 100 hours of untranscribed speech are used.