Name
Abstract | ||
|---|---|---|
Motivated by a desire to assess the prosody of foreign language learners, this study demonstrates the benefit of high-level syntactic information in automatically deciding where phrase breaks and pitch accents should go in text. The connection between syntax and prosody is well-established, and naturally lends itself to tree-based probabilistic models. With automatically-derived parse trees paired to tree transducer models, we found that categorical prosody tags for unseen text can be determined with significantly higher accuracy than they can with a baseline method that uses n-gram models of part-of-speech tags. On the Boston University Radio News Corpus, the tree transducer outperformed the baseline by 14% overall for accents, and by 3% overall for breaks. These automatic results fell within this corpus's range of inter-speaker agreement in assigning accents and breaks to text. |
Year | Venue | Keywords |
|---|---|---|
2011 | 12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | syntax, prosody, ToBI, TTS, tree transducers |
Field | DocType | Citations |
Prosody,Categorical variable,Computer science,Abstract syntax tree,Phrase,Speech recognition,Artificial intelligence,Natural language processing,Parsing,Probabilistic logic,Syntax,Foreign language | Conference | 1 |
PageRank | References | Authors |
0.36 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Tepperman | 1 | 73 | 8.59 |
| Emily Nava | 2 | 4 | 1.15 |