Name
Title | ||
|---|---|---|
A comparison of methods for speaker-dependent pronunciation tuning for text-to-speech synthesis |
Abstract | ||
|---|---|---|
Abstract Unit-based text-to-speech (TTS) systems typically use a set of speech recordings,that have been,phonetically transcribed to create a large set of phonetic units. During synthesis, pronunciations,for input text are generated and used to guide the selection of a sequence,of phonetic,units. The style of these system,pronunciations,must,match,the style of the phonetic,transcriptions of the recorded,speech,database,in order to maximize,the quality of the synthesized,speech. Furthermore, since different speakers have different speech characteristics, supporting multiple speakers for a single language,generally,requires,applying,a speaker-dependent mapping,to speaker-independent pronunciations.,This paper investigates three automatic,methods,for this process,of speaker-dependent pronunciation tuning: word N-grams, decision trees, and transformation-based learning. Transformation-based learning achieved the best results, lowering,the phone,error rate of the text pronunciations compared,to the speech transcriptions by 26% over the error rate of the unmodified,text transcriptions. |
Year | Venue | Keywords |
|---|---|---|
2005 | INTERSPEECH | error rate,decision tree,text to speech |
Field | DocType | Citations |
Pronunciation,Speech corpus,Transcription (linguistics),Decision tree,Speech synthesis,Computer science,Word error rate,Speech recognition,Phone,Natural language processing,Text to speech synthesis,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| gabriel webster | 1 | 5 | 1.30 |
| tina burrows | 2 | 5 | 1.32 |
| Kate Knill | 3 | 249 | 28.02 |
| guildhall st | 4 | 0 | 0.34 |