47 research outputs found
IMPROVING MULTIPLE-CROWD-SOURCED TRANSCRIPTIONS USING A SPEECH RECOGNISER
ABSTRACT This paper introduces a method to produce high-quality transcriptions of speech data from only two crowd-sourced transcriptions. These transcriptions, produced cheaply by people on the Internet, for example through Amazon Mechanical Turk, are often of low quality. Often, multiple crowd-sourced transcriptions are combined to form one transcription of higher quality. However, the state of the art is to use essentially a form of majority voting, which requires at least three transcriptions for each utterance. This paper shows how to refine this approach to work with only two transcriptions. It then introduces a method that uses a speech recogniser (bootstrapped on a simple combination scheme) to combine transcriptions. When only two crowd-sourced transcriptions are available, on a noisy data set this improves the word error rate to gold-standard transcriptions by 21 % relative
On the Effect of Fundamental Frequency on Amplitude and Frequency Modulation Patterns in Speech Resonances
On the effect of fundamental frequency on amplitude and frequency modulation patterns in speech resonances
Sherris?, Dorothy Tindal and Michael Terry at Hill Cottage, Armidale, New South Wales, May 1922/
Title devised by cataloguer from accompanying information.; Part of the collection: Michael Terry collection of negatives of his expeditions and travels, 1918-1971.; Condition: Loss.; Also available online at: http://nla.gov.au/nla.pic-vn6248470; Also available as a photograph: PIC Album 866
Rule-based grapheme-to-phoneme method for the Greek
This paper describes a trainable method for generating letter to sound rules for the Greek language, for producing the pronunciation of out-of-vocabulary words. Several approaches have been adopted over the years for grapheme-to-phoneme conversion, such as hand-seeded rules, finite state transducers, neural networks, HMMs etc, nevertheless it has been proved that the most reliable method is a rule-based one. Our approach is based on a semi-automatically pre-transcribed lexicon, from which we derived rules for automatic transcription. The efficiency and robustness of our method are proved by experiments on out-of-vocabulary words which resulted in over than 98% accuracy on a word-base criterion