A hybrid word / phoneme-based approach for improved vocabulary-independent search in spontaneous speech
Peng Yu, Frank Torsten Bernd Seide · 2004
For efficient organization of speech recordings – meetings, interviews, voice mails, and lectures – being able to search for spoken keywords is essential. Today, most spoken document retrieval systems use large-vocabulary recognition. For the above scenarios, such systems suffer from the unpredictable domain, out-ofvocabulary queries, and generally high word-error rate (WER). In [1], we presented a system for phonetic indexing and searching of spontaneous speech. It is vocabulary-independent and based on phoneme lattices. In the present paper, we propose to combine it with word-based search into a hybrid approach. We explore two methods of combination: posterior combination (merging search results of a word-based and a phoneme-based system) and prior combination (combining word and phoneme language models and vocabularies to form a hybrid recognizer). The search accuracy of our best purely phonetic baseline is 64% (Figure of Merit), and our purely word-based baselines are below 50%. The new hybrid approach achieves 73%, if the recognizer uses a language model that matches the test-set domain. With a mismatched language model, 71 % is achieved. Our results show that the proposed hybrid model benefits from the best of two worlds: Word-level language context and robustness of phonetic search to unknown words and domain mismatch. 1.