Lithuanian Broadcast Speech Transcription Using Semi-Supervised Acoustic Model Training

International audienceThis paper reports on an experimental work to build a speech transcriptionsystem for Lithuanian broadcast data, relying on unsupervised andsemi-supervised training methods as well as on other low-knowledge methods tocompensate for missing resources. Unsupervised acoustic model training isinvestigated using 360 hours of untranscribed speech data. A graphemicpronunciation approach is used to simplify the pronunciation model generationand therefore ease the language model adaptation for the system users.Discriminative training on top of semi-supervised training is alsoinvestigated, as well as various types of acoustic features and theircombinations. Experimental results are provided for each of our developmentsteps as well as contrastive results comparing various options. Using the bestsystem configuration a word error rate of 18.3\% is obtained on a set ofdevelopment data from the Quaero program