Discriminative reranking of ASR hypotheses with morpholexical and N-best-list features
2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Waikoloa, HI, Amerika Birleşik Devletleri, 11 - 15 Aralık 2011, ss.202-207, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/asru.2011.6163931
- Basıldığı Şehir: Waikoloa, HI
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.202-207
- Boğaziçi Üniversitesi Adresli: Evet
Özet
This paper explores rich morphological and novel n-best-list features for reranking automatic speech recognition hypotheses. The morpholexical features are defined over the morphological features obtained by using an n-gram language model over lexical and grammatical morphemes in the first-pass. The n-best-list features for each hypothesis are defined using that hypothesis and other alternate hypotheses in an n-best list. Our methodology is to align each hypothesis with other hypotheses one by one using minimum edit distance alignment. This gives us a set of edit operations - substitution, addition and deletion as seen in these alignments. These edit operations constitute our n-best-list features as indicator features. The reranking model is trained using a word error rate sensitive averaged perceptron algorithm introduced in this paper. The proposed methods are evaluated on a Turkish broadcast news transcription task. The baseline systems are word and statistical sub-word systems which also employ morphological features for reranking. We show that morpholexical and n-best-list features are effective in improving the accuracy of the system (0.8%). © 2011 IEEE.