Audio signal classification is usually done using conventional signal features such as mel-frequency cepstrum coefficients (MFCC), line spectral frequencies (LSF), and short time energy (STM). Learned dictionaries have been shown to have promising capability for creating sparse representation of a signal and hence have a potential to be used for the extraction of signal features. In this paper,...

The purpose of speech emotion recognition system is to classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral and happiness. Speech features that are commonly used in speech emotion recognition (SER) rely on global utterance level prosodic features. In our work, we evaluate the impact of frame-level feature extraction. The speech samples are fro...

Speech processing has emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speaker recognition is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major conc...

This paper presents a novel system that performs text-independent speaker authentication using new spiking neural network (SNN) architectures. Each speaker is represented by a set of prototype vectors that is trained with standard Hebbian rule and winner-takes-all approach. For every speaker there is a separated spiking network that computes normalized similarity scores of MFCC (Mel Frequency C...

In this paper a multi-channel speech enhancement framework for distant speech acquisition in noisy and reverberant environments for Non-negative Matrix Factorization (NMF)-based Automatic Speech Recognition (ASR) is proposed. The system is evaluated for its use in an assistive vocal interface for physically impaired and speech-impaired users. The framework utilises the Spatially Pre-processed S...

This paper presents a set of acoustic feature pre–processing techniques that are applied to improving automatic speech recognition (ASR) performance on the Aurora 2 noisy speech recognition task. The principal contribution of this paper is an approach for cepstrum domain feature compensation in ASR which is motivated by techniques for decomposing speech and noise that were originally developed ...

Keyword spotting (KWS) plays a crucial role in human–machine interactions involving smart devices. In recent years, temporal convolutional networks (TCNs) have performed outstandingly with less computational complexity, comparison classical neural network (CNN) methods. However, it remains challenging to achieve trade-off between small-footprint model and high accuracy for the edge deployment o...

Berbicara adalah cara komunikasi yang paling mudah dan banyak digunakan antara manusia. Pengembangan antarmuka komputer manusia untuk membangun dialog serupa mesin inspirasi di balik sistem pengenalan suara. Salah satu algoritma tersebut koefisien Cepstral frekuensi Mel. Makalah ini menjelaskan semua tahapan teknik MFCC bersama dengan deskripsi singkat dari setiap proses. Dalam penelitian dijel...

Voice signals are the essential input source for applications based on human and computer interaction technology. Gender identification through voice is one of most challenging tasks. For signal analysis, deep learning algorithms provide an alternative to traditional conventional classification. To identify gender female, male ‘first-time’ transgender, algorithm used improve robustness model wi...

This research was conducted to develop a method to identify voice utterance. For voice utterance that encounters change caused by aging factor, with the interval of 10 to 25 years. The change of voice utterance influenced by aging factor might be extracted by MFCC (Mel Frequency Cepstrum Coefficient). However, the level of the compatibility of the feature may be dropped down to 55%. While the o...