Speaker Verification Using Autoregressive Spectrum of Speech Signal in Composite Vector Stochastic Processes Model Representation

Abstract An important application of speech processing is speaker recognition, which automatically recognizes the person speaking in an audio recording, basis of which is speaker-specific information included in its speech features. It involves speaker verification and speaker identification. This paper presents an efficient method based on discrete wavelet transform and optimized variance spectral flux to enhance the enactment of speaker identification system. An effective feature extraction technique uses Daubechies 40 (db40) wavelet to compress and de-noised the speech signal by its decomposition into approximations and details coefficients at level 1. The approximation coefficients contain 99.9% of speech information as compared to detailed coefficients. So, the optimized variance spectral flux is applied on wavelet approximation coefficients which efficiently extract the frequency contents of the speech signal and gives unique features. The distance between extracted features has been obtained by applying traditional Bayesian information criteria. Experimental results were computed on recording data of 33 speakers (23 female and 10 males) for text independent identification of speaker. Evaluation of effectiveness of the proposed system is done by applying detection error trade-off curves, receiver operating characteristic, and area under curve. It shows 94.38% of speaker identification results when compared with traditional method using Mel frequency spectral coefficients which is 90.70%.

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

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00385 ◽

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.

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

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod21.2.44 ◽

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.

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