FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W70888257> ?p ?o ?g. }

• W70888257 abstract "In order to better understand different speech synthesis techniques on a common dataset, we devised a challenge that will help us better compare research techniques in building corpusbased speech synthesizers. In 2004, we released the first two 1200-utterance single-speaker databases from the CMU ARCTIC speech databases, and challenged current groups working in speech synthesis around the world to build their best voices from these databases. In January of 2005, we released two further databases and a set of 50 utterance texts from each of five genres and asked the participants to synthesize these utterances. Their resulting synthesized utterances were then presented to three groups of listeners: speech experts, volunteers, and US English-speaking undergraduates. This paper summarizes the purpose, design, and whole process of the challenge. 1. Background With a view to allowing closer comparison of corpus-based techniques, from labeling, pruning, join costs, signal processing techniques, and others, we devised a challenge for participants to use the same databases to synthesize utterances from a small number of genres. An organized evaluation, based on listening tests, was then carried out to try to rank the systems and help identify the effectiveness of the techniques. The sister field of speech recognition has clearly benefited from the availability of common datasets in order to provide valid comparisons between systems [1]. These evaluations concentrate efforts in the speech recognition fields, particularly through the 1990s with DARPA workshops where NIST (and others) devised standardized tests for speech recognition. It is clear that these standardized tests and widely available datasets allowed speech recognition results to be more easily compared and more importantly cause the core technology to improve. Although today many may criticize a naive word error metric as a sole accuracy measure for speech recognition systems, few would complain that it has not contributed to drastic improvement in the utility of speech recognition as a viable technology. Speech synthesis has not been as lucky in having a welldefined evaluation metric, nor has it had a well-funded centralized community that could be targeted to the same task. With the rise of general corpus-based speech synthesis over the last ten years, we have moved from a domain where new synthetic voices could only be built with many man-years of effort from highly skilled researchers. Such systems were tuned to the particular data sets being used, thus comparisons of techniques such as labeling and signal processing could only be done within the research group that originally developed the dataset. Such tying of databases to particular systems made it hard to genuinely compare techniques since the quality of the original recorded voice itself contributed greatly to the resulting synthetic voice quality." @default.
• W70888257 created "2016-06-24" @default.
• W70888257 creator A5046364646 @default.
• W70888257 creator A5050450986 @default.
• W70888257 date "2005-09-04" @default.
• W70888257 modified "2023-10-14" @default.
• W70888257 title "The blizzard challenge - 2005: evaluating corpus-based speech synthesis on common datasets" @default.
• W70888257 cites W187033940 @default.
• W70888257 cites W1970354675 @default.
• W70888257 cites W1984317582 @default.
• W70888257 cites W2084609288 @default.
• W70888257 cites W58567859 @default.
• W70888257 doi "https://doi.org/10.21437/interspeech.2005-72" @default.
• W70888257 hasPublicationYear "2005" @default.
• W70888257 type Work @default.
• W70888257 sameAs 70888257 @default.
• W70888257 citedByCount "107" @default.
• W70888257 countsByYear W708882572012 @default.
• W70888257 countsByYear W708882572013 @default.
• W70888257 countsByYear W708882572014 @default.
• W70888257 countsByYear W708882572015 @default.
• W70888257 countsByYear W708882572016 @default.
• W70888257 countsByYear W708882572017 @default.
• W70888257 countsByYear W708882572018 @default.
• W70888257 countsByYear W708882572020 @default.
• W70888257 countsByYear W708882572021 @default.
• W70888257 countsByYear W708882572022 @default.
• W70888257 countsByYear W708882572023 @default.
• W70888257 crossrefType "proceedings-article" @default.
• W70888257 hasAuthorship W70888257A5046364646 @default.
• W70888257 hasAuthorship W70888257A5050450986 @default.
• W70888257 hasConcept C14999030 @default.
• W70888257 hasConcept C154945302 @default.
• W70888257 hasConcept C204321447 @default.
• W70888257 hasConcept C28490314 @default.
• W70888257 hasConcept C41008148 @default.
• W70888257 hasConceptScore W70888257C14999030 @default.
• W70888257 hasConceptScore W70888257C154945302 @default.
• W70888257 hasConceptScore W70888257C204321447 @default.
• W70888257 hasConceptScore W70888257C28490314 @default.
• W70888257 hasConceptScore W70888257C41008148 @default.
• W70888257 hasLocation W708882571 @default.
• W70888257 hasOpenAccess W70888257 @default.
• W70888257 hasPrimaryLocation W708882571 @default.
• W70888257 hasRelatedWork W2130043461 @default.
• W70888257 hasRelatedWork W2350741829 @default.
• W70888257 hasRelatedWork W2358668433 @default.
• W70888257 hasRelatedWork W2376932109 @default.
• W70888257 hasRelatedWork W2382290278 @default.
• W70888257 hasRelatedWork W2390279801 @default.
• W70888257 hasRelatedWork W2748952813 @default.
• W70888257 hasRelatedWork W2899084033 @default.
• W70888257 hasRelatedWork W3192589309 @default.
• W70888257 hasRelatedWork W2530322880 @default.
• W70888257 isParatext "false" @default.
• W70888257 isRetracted "false" @default.
• W70888257 magId "70888257" @default.
• W70888257 workType "article" @default.