The Vocal Joystick Vowel Corpus, by Washington University, was used to study monophthongs pronounced by native English speakers. The objective of this study was to quantitatively measure the extent at which speech recognition methods can distinguish between similar sounding vowels. In particular, the phonemes /@/, /æ/, /A:/ and /2/ were analysed. 748 sound files from the corpus were used and su...

Digital processing of speech signal and speaker recognition algorithm is very important for fast and accurate automatic voice recognition technology. A direct analysis of the voice signal is complex due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. The non-para...

Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV Units formed with Stop Consonant – Short Vowel (SCSV) were considered for classification task. The work ca...

Economical speaker recognition solution from degraded human voice signal is still a challenge. This article covering results of an experiment which targets to improve feature extraction method for effective identification audio with the help data science. Every speaker’s has identical characteristics. Human ears can easily identify these different characteristics and classify audio. Mel-Frequen...

Input level fusion and output level fusion methods are compared for fusing Mel-frequency Cepstral Coefficients with their corresponding delta coefficients. A 49 speaker subset of the King database is used under wideband and telephone conditions. The best input level fusion system is more computationally complex than the output level fusion system. Both input and output fusion systems were able ...

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice...

Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) are the most popular acoustic features used in speech recognition. Often it depends on the task, which of the two methods leads to a better performance. In this work we develop acoustic features that combine the advantages of MFCC and PLP. Based on the observation that the techniques have many similarities, we rev...

In the present study, a new feature extraction method based on relative spectra and gammachirp auditory filterbank is proposed for robust noisy speech recognition. The relative spectra filtering are applied to the log of the output of the gammachirp filterbank which incorporates the properties of the cochlear filter in order to remove uncorrelated additive noise components. The performances of ...

In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested...

In this paper, the use of sonority measure as an acoustic feature of the speech signal for continuous automatic speech recognition is described. The representation of sonority extent of sounds is made with a help of spectrum derivation. Therefore, a novel articulatory motivated acoustic feature expressing the sonority is named spectrum derivative feature. The new feature is tested in combinatio...