Some issues affecting the transcription of Hungarian broadcast audio

This paper reports on a speech-to-text (STT) transcription system for Hungarian broadcast audio developed for the 2012 Quaero evaluations. For this evaluation, no manually transcribed audio data were provided for model training, however a small amount of development data were provided to assess system performance. As a consequence, the acoustic models were developed in an unsupervised manner, with the only supervision provided indirectly by the language model. The language models were trained on texts downloaded from various websites, also without any speech transcripts. This contrasts with other STT systems for Hungarian broadcast audio which use at least 10 to 50 hours of manually transcribed data for acoustic training, and typically include speech transcripts in the language models. Based on mixed results previously reported applying morph-based approaches to agglutinative languages such as Hungarian, word-based language models were used. The initial Word Error Rate (WER) of the system using contextindependent seed models from other languages of 59.8% on the 3h development corpus was reduced to 25.0% after successive training iterations and system refinement. The same system obtained a WER of 23.3% on the independent Quaero 2012 evaluation corpus (a mix of broadcast news and broadcast conversation data). These results compare well with previously reported systems on similar data. Various issues affecting system performance are discussed, such as amount of training data, the acoustic features and choice of text sources for language model training. Index Terms: Large vocabulary continuous speech recognition (LVCSR), broadcast news transcription, Hungarian language, unsupervised training, agglutinative languages, Bottleneck MLP features