Building an ASR Corpus Based on Bulgarian Parliament Speeches

Diana Geneva, Georgi Shopov, Stoyan Mihov

Abstract: This paper presents the methodology we applied for building a new corpus of Bulgarian speech suitable for training and evaluating modern speech recognition systems. The Bulgarian Parliament ASR (BG-PARLAMA) corpus is derived from the recordings of the plenary sessions of the Bulgarian Parliament. The manually transcribed texts and the audio data of the speeches are processed automatically to build an aligned and segmented corpus. NLP tools and resources for Bulgarian are utilized for the language specific tasks. The resulting corpus consists of 249 hours of speech from 572 speakers and is freely available for academic use. First experiments with an ASR system trained on the BG-PARLAMA corpus have been conducted showing word error rate of around 7% on parliament speeches from unseen speakers using time-delay deep neural network (TD-DNN) architecture. The BG-PARLAMA corpus is to our knowledge the largest speech corpus currently available for Bulgarian.