We propose a method for automatically generating a pronunciation dictionary based on a pronunciation neural network that can predict plausible pronunciations (realized pronunciations) from canonical pronunciations. This method can generate multiple forms of realized pronunciations using the pronunciation network. Experimental results on spontaneous speech show that the automatically-derived pro...

This paper presents a new approach for grapheme-to-phoneme conversion based on morphology. With this approach, a high accuracy can be obtained, although not for all words a transcription is achieved. The principle of this approach is to automatically decompose an existing pronunciation lexicon into morpheme-similar units called pseudo-morphological units. The pronunciation of the pseudo-morphol...

In this paper, we present a novel method to regulate lexical connections among morpheme-based pronunciation lexicons for Korean large vocabulary continuous speech recognition (LVCSR) systems. A pronunciation dictionary plays an important role in subword-based LVCSR in that pronunciation variations such as coarticulation will deteriorate the performance of an LVCSR system if it is not well accou...

The utilize of YouTube for teaching and learning English in this era is needed toimplement. This research aims to investigated Channel: Pronunciation withEmma able enhance students’ pronunciation at SMK Bina Mandiri Bekasi. designof Classroom Action Research (CAR) uses a quantitative descriptivemethod. Participants were 36 students from XI TKJ F MandiriBekasi. result showed that using Channel:P...

Observing that most variations in pronunciation are strongly speaker and speaking style dependent, and that the introduction of pronunciation variants in a speaker-independent recognition system is of limited success, we refrain from applying multiple pronunciation variants in the speakerindependent case and instead introduce pronunciation variants without supervision when specializing the reco...

Pronunciation dictionaries are the interface between orthographic and phonetic representation of the speech signal and are thereby a substantial component of speech recognition systems. In many systems simple canonical pronunciation forms are used within the dictionary. They represent the “correct” pronunciation as they are found in lexicons and neither contain the most frequent pronunciation n...

Modelling of pronunciation variability is an important part of the acoustic model of a speech recognition system. Good pronunciation models contribute to the robustness and portability of a speech recogniser. Usually pronunciation modelling is associated with the recognition lexicon which allows a direct control of HMM selection. However, in state-of-the-art systems the use of clustering techni...

Calculating posterior probability within a standard pronunciation space (SPS) is a common method in automatic pronunciation error detection (APED). However, to pronunciation errors outside the SPS, this kind of methods can only give an approximate solution, that may be not right in many applications. This paper expands the SPS to include more pronunciation errors, introduces a Bhattacharyya dis...