An Analysis of Speech Signal for Multiple Languages through Spectrum Analysis

2017 ◽  
Vol 3 (4) ◽  
pp. 22
Author(s):  
Yadav Pooja ◽  
Jain VinayKumar ◽  
◽  
Keyword(s):  
Spectrum Analysis ◽  
Speech Signal ◽  
Multiple Languages

Speech Synthesis Using Damped Sinusoids

2002 ◽  
Vol 45 (4) ◽  
pp. 639-650 ◽  
Author(s):  
James M. Hillenbrand ◽  
Robert A. Houde
Keyword(s):  
Spectrum Analysis ◽  
Speech Signal ◽  
Speech Synthesis ◽  
Fourier Spectrum ◽  
Fundamental Period ◽  
Speech Synthesizer ◽  
Perceptual Evaluation ◽  
Random Intervals ◽  
Sentence Intelligibility ◽  
Nonsense Syllables

A speech synthesizer was developed that operates by summing exponentially damped sinusoids at frequencies and amplitudes corresponding to peaks derived from the spectrum envelope of the speech signal. The spectrum analysis begins with the calculation of a smoothed Fourier spectrum. A masking threshold is then computed for each frame as the running average of spectral amplitudes over an 800-Hz window. In a rough simulation of lateral suppression, the running average is then subtracted from the smoothed spectrum (with negative spectral values set to zero), producing a masked spectrum. The signal is resynthesized by summing exponentially damped sinusoids at frequencies corresponding to peaks in the masked spectra. If a periodicity measure indicates that a given analysis frame is voiced, the damped sinusoids are pulsed at a rate corresponding to the measured fundamental period. For unvoiced speech, the damped sinusoids are pulsed on and off at random intervals. A perceptual evaluation of speech produced by the damped sinewave synthesizer showed excellent sentence intelligibility, excellent intelligibility for vowels in /hVd/ syllables, and fair intelligibility for consonants in CV nonsense syllables.

scholarly journals Word recognition from speech signal using linear predictive coding and spectrum analysis

2018 ◽  
Vol 7 (3) ◽  
pp. 1531
Author(s):  
Mandeep Singh ◽  
Gurpreet Singh
Keyword(s):  
Word Recognition ◽  
Sample Size ◽  
Spectrum Analysis ◽  
Speech Signal ◽  
Predictive Coding ◽  
The Other ◽  
Hybrid Technique ◽  
Linear Predictive Coding ◽  
Statistical Parameters ◽  
Isolated Word

This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.  

Perceptual Learning of Vocoded Speech With and Without Contralateral Hearing: Implications for Cochlear Implant Rehabilitation

2020 ◽  
pp. 1-10
Author(s):  
Martin Chavant ◽  
Alexis Hervais-Adelman ◽  
Olivier Macherey
Keyword(s):  
Cochlear Implant ◽  
Perceptual Learning ◽  
Speech Signal ◽  
Training Phase ◽  
Monosyllabic Words ◽  
Low Pass ◽  
Contralateral Ear ◽  
Number Of Individuals ◽  
Insight Into ◽  
Vocoded Speech

Purpose An increasing number of individuals with residual or even normal contralateral hearing are being considered for cochlear implantation. It remains unknown whether the presence of contralateral hearing is beneficial or detrimental to their perceptual learning of cochlear implant (CI)–processed speech. The aim of this experiment was to provide a first insight into this question using acoustic simulations of CI processing. Method Sixty normal-hearing listeners took part in an auditory perceptual learning experiment. Each subject was randomly assigned to one of three groups of 20 referred to as NORMAL, LOWPASS, and NOTHING. The experiment consisted of two test phases separated by a training phase. In the test phases, all subjects were tested on recognition of monosyllabic words passed through a six-channel “PSHC” vocoder presented to a single ear. In the training phase, which consisted of listening to a 25-min audio book, all subjects were also presented with the same vocoded speech in one ear but the signal they received in their other ear differed across groups. The NORMAL group was presented with the unprocessed speech signal, the LOWPASS group with a low-pass filtered version of the speech signal, and the NOTHING group with no sound at all. Results The improvement in speech scores following training was significantly smaller for the NORMAL than for the LOWPASS and NOTHING groups. Conclusions This study suggests that the presentation of normal speech in the contralateral ear reduces or slows down perceptual learning of vocoded speech but that an unintelligible low-pass filtered contralateral signal does not have this effect. Potential implications for the rehabilitation of CI patients with partial or full contralateral hearing are discussed.

“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

2011 ◽  
Vol 21 (2) ◽  
pp. 44-54
Author(s):  
Kerry Callahan Mandulak
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Clinical Utility ◽  
Acoustic Analysis ◽  
Moment Analysis ◽  
Analysis Tool ◽  
Spectral Moment ◽  
Clinical Measure ◽  
Perceptual Analysis ◽  
Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

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