We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person's normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sou...

This paper proposes a new investigation on Gaussian mixture model (GMM) by comparing it with some preliminary experiments on multilayered perceptron network (MLP) with backpropagation learning algorithm (BKP) and dynamic time warping (DTW) techniques on Thai text-dependent speaker identification system. Three major identification engines are conducted on 50 speakers with isolated digits 0-9. Tr...

This paper investigates the adaptation of automatic speech recognition to disease detection by analyzing the voice parameters. The analysis of the voice allows the identification of the diseases which affect the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. This paper presents a novel method to keep track of patient’s patholog...

The short-term temporal information in speech is widely used for automatic speech recognition (ASR) systems in the form of dynamic features. Long-term temporal information has also been focused on recently and is used to complement traditional short-term features (typically from 25 to 100 ms). There are several approaches to represent long-term temporal information in ASR systems. However, thos...

Speech recognition in reverberant environments is still a challenge problem. In this paper, we first investigated the reverberation effect on subband temporal envelopes by using the modulation transfer function (MTF). Based on the investigation, we proposed an algorithm which normalizes the subband temporal modulation spectrum (TMS) to reduce the diffusion effect of the reverberation. During th...

This paper is concerned with the development of Back-propagation Neural Network for Bangla Speech Recognition. In this paper, ten bangla digits were recorded from ten speakers and have been recognized. The features of these speech digits were extracted by the method of Mel Frequency Cepstral Coefficient (MFCC) analysis. The mfcc features of five speakers were used to train the network with Back...

We study the problem of vocal effort mismatch in speaker verification. Changes in speaker’s vocal effort induce changes in fundamental frequency (F0) and formant structure which introduce unwanted intra-speaker variations to features. We compare seven alternative spectrum estimators in the context of melfrequency cepstral coefficient (MFCC) extraction for speaker verification. The compared vari...

Automatic Speech Recognition has been a goal of research for many decades. Many research works have been developed successfully for automatic speech recognition (ASR) of English language. ASR for European languages has not reached their height as ASR in English language. In this work, an implementation of Tamil based automatic speech Recognition System is developed. The ASR has many phases to p...

Due to various factors, the vast majority of the research in the field of Acoustic Scene Classification has used monaural or binaural datasets. This paper introduces EigenScape a new dataset of 4th-order Ambisonic acoustic scene recordings and presents preliminary analysis of this dataset. The data is classified using a standard Mel-Frequency Cepstral Coefficient Gaussian Mixture Model system, ...

Neha Chauhan Birla Institute of Technology, Mesra, Ranchi Abstract— Speaker Recognition is the computing task of validating a user’s claimed identity using speech characteristics. Main objective of speech recognition system is to communication with a device through our voice. Mel frequency Cepstral Coefficient (MFCC) features are combined with pitch and root mean square values and tested for im...