Comparative Study on Sentence Boundary Prediction for German and English Broadcast News

We present a comparative study on sentence boundary prediction for German and English broadcast news that explores generalization across different languages. In the feature extraction stage, word pause duration is firstly extracted from word aligned speech, and forward and backward language models are utilized to extract textual features. Then a gradient boosted machine is optimized by grid search to map these features to punctuation marks. Experimental results confirm that word pause duration is a simple yet effective feature to predict whether there is a sentence boundary after that word. We found that Bayes risk derived from pause duration distributions of sentence boundary words and non-boundary words is an effective measure to assess the inherent difficulty of sentence boundary prediction. The proposed method achieved F-measures of over 90% on reference text and around 90% on ASR transcript for both German broadcast news corpus and English multi-genre broadcast news corpus. This demonstrates the state of the art performance of the proposed method

Automatic sentence boundary detection for German broadcast news

In this work we aim at enriching the transcript of an automatic speech recognition system with punctuation by automatically detecting sentence ends. We make use of a simple word-based language model and combine it with a decision tree for the acoustic features of speech. The focus lies on selecting robust acoustic features that reflect the prosodic characteristics of the German language in a most optimal way. We arrive at a Sentence Unit Error Rate of 54 compared to the state-of-the art rate for English of 61, by applying a comparable detection system. This is a sound indication that prosody has a stronger cue on perception of sentence boundaries for German than for English. Our work is, to our knowledge, the first system developed for sentence boundary detection for the broadcast news dom ain for German language. Our results can therefore serve as a baseline for further studies in this scenario