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Journal ArticleDOI

Admissible wavelet packet features based on human inner ear frequency response for Hindi consonant recognition

TLDR
A new filter structure using admissible wavelet packet analysis is proposed for Hindi phoneme recognition using a Hidden Markov Model (HMM) based classifier and shows better performance than conventional features for Hindi consonant recognition.
About
This article is published in Computers & Electrical Engineering.The article was published on 2014-05-01. It has received 39 citations till now. The article focuses on the topics: Wavelet packet decomposition & Wavelet.

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Citations
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Journal ArticleDOI

Feature extraction technique using ERB like wavelet sub-band periodic and aperiodic decomposition for TIMIT phoneme recognition

TL;DR: This frontend feature processing technique employs equivalent rectangular bandwidth (ERB) filter like wavelet speech feature extraction method called Wavelet ERB Sub-band based Periodicity and Aperiodicity Decomposition (WERB-SPADE), and examines its validity for TIMIT phone recognition task in noisy environments.
Journal ArticleDOI

Multiple cameras audio visual speech recognition using active appearance model visual features in car environment

TL;DR: The shape and appearance information are extracted from jaw and lip region to enhance the performance in vehicle environments to show more robustness compared to acoustic speech recognizer across all driving conditions.
Journal ArticleDOI

GFCC based discriminatively trained noise robust continuous ASR system for Hindi language

TL;DR: The proposed work uses noise robust method Gammatone Frequency Cepstral Coefficients (GFCC) for feature extraction, trigram language modeling, and HMM-Gaussian mixture model (GMM) based acoustic modeling to implement a continuous Hindi language ASR system.
Journal ArticleDOI

ASRoIL: a comprehensive survey for automatic speech recognition of Indian languages

TL;DR: The purpose of this systematic survey is to sum up the best available research on automatic speech recognition of Indian languages that is done by synthesizing the results of several studies by analyzing the possible opportunities, challenges, techniques, methods and the evidence from studies.
Journal ArticleDOI

Adaptive frequency scaled wavelet packet decomposition for frog call classification

TL;DR: In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification, which outperforms all other comparable methods.
References
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Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences

TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
Journal ArticleDOI

Perceptual linear predictive (PLP) analysis of speech

TL;DR: A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, which uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum, and yields a low-dimensional representation of speech.
Book

An Introduction to the Psychology of Hearing

TL;DR: In this paper, the nature of sound and the structure and function of the auditory system are discussed, including absolute thresholds, frequency selectivity, masking and the critical band, and the perception of loudness.
Book

A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

TL;DR: The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.
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