The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using i...

Acoustic feature extraction from speech constitutes a fundamental component of automatic speech recognition (ASR) systems. In this paper, we propose a novel feature extraction algorithm, perceptual-MVDR (PMVDR), which computes cepstral coefficients from the speech signal. This new feature representation is shown to better model the speech spectrum compared to traditional feature extraction appr...

This study reports the analysis of incorrect responses from a spoken word recognition experiment of 1,428 words, designed to be a representative sample of the entire English lexicon. The stimuli were presented in six-talker babble to 193 normal-hearing listeners at three signal-to-noise ratios (0, 5, and 10 dB). The results reveal several patterns: (1) Errors tend to be of higher frequency than...

A powerful theoretical framework for exploring recognition memory is the global matching framework, in which a cue's memory strength reflects the similarity of the retrieval cues being matched against the contents of memory simultaneously. Contributions at retrieval can be categorized as matches and mismatches to the item and context cues, including the self match (match on item and context), i...

The relative abilities of word frequency, contextual diversity, and semantic distinctiveness to predict accuracy of spoken word recognition in noise were compared using two data sets. Word frequency is the number of times a word appears in a corpus of text. Contextual diversity is the number of different documents in which the word appears in that corpus. Semantic distinctiveness takes into acc...

When a speech recognition system is used in a real environment, its recognition performance is affected by the surrounding noise. Most types of additional noise as well as SNRs are difficult to predict, so there is a mismatch between the training and test data. We need a method to deal with this problem. In this paper, we propose an HMM compositionbased model adaptation method with a priori noi...

Automatic Speech Recognition (ASR) is a technology for identifying uttered word(s) represented as an acoustic signal. However, one of the important aspects of a noise-robust ASR system is its ability to recognise speech accurately in noisy conditions. This paper studies the applications of Multi-Nets Artificial Neural Networks (M-N ANNs), a realisation of multiple-views multiple-learners approa...

In the present paper we show that a general-purpose word learn­ ing model can simulate several important findings from recent experiments in language acquisition. Both the addition of back­ ground noise and varying the speaker have been found to in­ fluence infants’ performance during word recognition experi­ ments. We were able to replicate this behaviour in our artificial word learning agent....

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20...