The speech signal is modeled using zerocrossing interval distribution of the signal in time domain. The distributions of these parameters are studied over five Malayalam (one of the most popular Indian language) vowels. We found that the distribution patterns are almost similar for repeated utterances of the same vowel and varies from vowel to vowel. These distribution patterns are used for rec...

In this article, we conduct a study on the performance of some supervised learning algorithms for vowel recognition. This study aims to compare the accuracy of each algorithm. Thus, we present an empirical comparison between five supervised learning classifiers and two combined classifiers: SVM, KNN, Naive Bayes, Quadratic Bayes Normal (QDC) and Nearst Mean. Those algorithms were tested for vow...

We investigate how supervision (in the form of explicit instruction) interacts with distributional learning in the acquisition of the perception of a novel vowel contrast in a second language. An experiment with non-Dutch-speaking Bulgarians reveals that listeners who receive bimodal distributional training without explicit instruction can acquire new Dutch vowel contrasts, and that listeners w...

In spoken word recognition, one of the crucial point is to identify the vowel phonemes. Vowel phonemes are used to combine two or more consonant phonemes in most of the words spoken, and the meaning of the words changes with the change of vowels. Therefore, in order to recognize a word, identification of vowel phoneme is as important as the identification of constituent consonant phonemes. This...

SYREN is a connectionist model that uses temporal information in a speech signal for syllable recognition. It classifies the rates and directions of formant center transitions, and uses an adaptive method to associate transition events with each syllable. The system uses explicit spatial temporal representations through delay lines. SYREN uses implicit parametric temporal representations in for...

In this paper we discuss the significance of Vowel Onset Point (VOP) for speech recognition in particular, and for accurate speech analysis in general. Vowel onset point is the instant at which the vowel part starts in a ConsonantVowel (CV) utterance. The syllable-like CV utterances are basic units for both speech production and speech recognition. Recognition performance of isolated utterances...

Tomoko Okuno, University of Michigan Debra M. Hardison, Michigan State University This study examined factors affecting perception training of vowel duration in L2 Japanese with transfer to production. In a pre-test, training, post-test design, 48 L1 English speakers were assigned to one of three groups: auditory-visual (AV) training using waveform displays, auditory-only (A-only), or no traini...

The Tonal and Speech Materials for Auditory Perceptual Assessment, Disc 1.0 audio compact disc contains a set of consonant, vowel-nucleus, consonant speech materials that have been segmented at the approximate phoneme boundaries so that the carrier phrase and consonant segments of a word can be presented to one ear and the carrier phrase and vowel segment of the word presented to the other ear....

Toddlers' and preschoolers' knowledge of the phonological forms of words was tested in Spanish-learning, Catalan-learning, and bilingual children. These populations are of particular interest because of differences in the Spanish and Catalan vowel systems: Catalan has two vowels in a phonetic region where Spanish has only one. The proximity of the Spanish vowel to the Catalan ones might pose sp...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...