Minimal-resource phonetic language models to summarize untranscribed speech

We propose to extract summary sentences from lexically untranscribed speech via phone tokenization. We use decoded phone sequences instead of words to train language models to infer semantically significant utterances. Phone tokens yield comparable results to words on the TDT-2 English corpus, yet require significantly less linguistic resources - no need for automatic speech recognition (ASR): (1) Using decoded phones of high phone error rate (78.7%) leads to comparable results to using ASR-decoded words. (2) Tokenizing English audio using a Czech phone recognizer leads to comparable results to using English words from closed-captions. These trends parallel those established in spoken language recognition and have practical significance: we can potentially summarize speech passages of resource-poor languages by leveraging existing tools developed on resource-rich languages.