The output of the forced alignment can then be used to create other tiers on other phonological levels. Using a hidden markov model (hmm) recognition system, forced alignment can be used together with phoneme models and the Viterbi algorithm. ![]() When it comes to dynamic time warping, the signal is compared and aligned with a reference from for example a text to speech system. Successful attempts have been made using hidden markov models (Brugnara et al., 1993) and dynamic time warping (Malfrére et al., 19), both well-known techniques in ASR. To be able to implement a new language for automatic aligning within the framework there is a need for some kind of grapheme to phone converter and a trained Hidden Markov Model that can be used by the viterbi recognition program HVite from the HTK toolkit (Young et al., 2006).Īligning recorded speech automatically is a technique that borrows heavily from automatic speech recognition (ASR). On top of the source code a built in scripting language can execute commands, make calculations and communicate with other programs in different manners (Boersma & Weenink, 2007). Praat is distributed as an open source software under a GPL license. In this case, a framework forĭoing automatic aligning, called Easyalign, was developed for the free software Praat (Goldman, 2007). To be able to perform automatic aligning common speech recognition techniques are applied at various levels. Even if the aligning sometimes is very crude, it certainly facilitates the tedious manual work of labeling and transcribing. This orthographic transcription can then be used together with the sound file to get a crude overview of where in the recording certain events occur according to the orthographic transcription. It is also a very useful tool in forensic speaker identification as one often receives a tapped recording together with an orthographic transcription. To automatically align text and sound is enormously valuable when one is handling large speech databases, either for research or for developing speech technology tools such as automatic speech recognition or text to speech systems. The result, to some extent successful, and conclusion invites to more research and developments for the future. With a Swedish grapheme to phone converter, a Swedish trained hidden markov model and the viterbi function HVite from the toolkit HTK, automatic aligning of an authentic forensic phonetic recording and corresponding orthographic transcription was produced. Using the free software Praat a plugin framework for automatic aligning, Easyalign, has been developed. Handling speech databases of various kinds and developing speech technology tools most often demands some kind of aligning. ![]() Você precisa do JavaScript ativado para vê-lo.Īutomatically aligning sound and text on phone, syllable, word and phrase level is a valuable tool. Jonas Lindh, Department of Linguistics, Göteborg University, Sweden.Įste endereço de email está sendo protegido de spambots. Semi-Automatic Aligning of Swedish Forensic Phonetic Phone Speech in Praat using Viterbi Recognition and HMM
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