Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with...

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

Speech emotion recognition is one of the latest challenges in speech processing and Human Computer Interaction (HCI) in order to address the operational needs in real world applications. Besides human facial expressions, speech has proven to be one of the most promising modalities for automatic human emotion recognition. Speech is a spontaneous medium of perceiving emotions which provides in-de...

We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spec...

The analysis and classification of audio signals are becoming increasingly important, especially in the age communication dissemination information through radio broadcasting systems. It is therefore essential that systems platforms available to monitor spread fake or fraudulent news. A speech feature-based correlation (SFC) algorithm a recognition framework developed this study, combining spec...

The most popular set of parameters used in recognition systems is the me1 frequency cepstral cocfficicnts. While giving generally good results, it remains that the filtering process, as used in the evaluation of these parameters, reduces the signal resolution in the frequency domain, which can have some impact in discriminating between phonemes. This paper presents a new parameterization approa...

Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described ...

We propose a method for integrating speech recognition and generation within a unified framework. The method consists of STRAIGHT, warped-frequency DCT, and an HMM engine. The warped-frequency DCT is used to derive a kind of mel-cepstral coefficient from the smoothed spectrum of STRAIGHT, which is known as a high-quality vocoder. This analysis/synthesis method has potential to improve the perfo...

Previous works have shown that remarkable performance improvements can be attained in speaker and language recognition tasks by combining several heterogeneous systems that provide complementary information. In this work, the complementarity of several i-vector language recognition systems, using Mel-Frequency Cepstral-Coefficient (MFCC) features computed on ShortTime Fourier Analysis windows o...

This paper introduces a new articulation rate filter and reports its combination with recently proposed constant Q cepstral coefficients (CQCCs) in their first application to automatic speaker verification (ASV). CQCC features are extracted with the constant Q transform (CQT), a perceptually-inspired alternative to Fourier-based approaches to time-frequency analysis. The CQT offers greater freq...