Automatic punctuation generation for speech

Automatic generation of punctuation is an essential feature for many speech-to-text transcription tasks. This paper describes a maximum a-posteriori (MAP) approach for inserting punctuation marks into raw word sequences obtained from automatic speech recognition (ASR). The system consists of an ¿acoustic model¿ (AM) for prosodic features (actually pause duration) and a ¿language model¿ (LM) for text-only features. The LM combines three components: an MLP-based trigger-word model and a forward and a backward trigram punctuation predictor. The separation into acoustic and language model allows to learn these models on different corpora, especially allowing the LM to be trained on large amounts of data (text) for which no acoustic information is available. We find that the trigger-word LM is very useful, and further improvement can be achieved when combining both prosodic and lexical information. We achieve an F-measure of 81.0% and 56.5% for voicemails and podcasts, respectively, on reference transcripts, and 69.6% for voicemails on ASR transcripts.