The speech signal enhancement is for obtain clean speech signal from noisy signal environment. For multimodal optimization we better to use natural-inspired algorithms. In this paper we use Firefly algorithmWe compare the firefly algorithm with particle swarm optimization technique (PSO). The proposed algorithm contains three important module techniques. Those are preprocessing module, optimiza...

BackgroundMultiple Sclerosis (MS) pathology is likely to disrupt central auditory pathways, thereby affecting an individual's ability discriminate speech from noise. Despite the importance of discrimination in daily communication, it's characterization context MS remains limited. This cross-sectional study evaluated under "real world" conditions where sentences were presented ecologically valid...

The purpose of this mixed model design was to examine recognition performance differences when measuring speech recognition in multitalker babble on listeners with normal hearing (n = 36) and listeners with hearing loss (n = 72) utilizing stimulus of varying linguistic complexity (digits, words, and sentence materials). All listeners were administered two trials of two lists of each material in...

The effects on speech intelligibility of three different noise reduction algorithms (spectral subtraction, minimal mean squared error spectral estimation, and subspace analysis) were evaluated in two types of noise (car and babble) over a 12 dB range of signal-to-noise ratios (SNRs). Results from these listening experiments showed that most algorithms deteriorated intelligibility scores. Modeli...

Most of the speech enhancement algorithms use the magnitude of STFT while phase is kept unchanged [3]. In this paper the magnitude of STFT of noisy speech is kept unchanged while the phase is modified. Modified complex spectrum of speech is obtained by combining unchanged magnitude spectrum and modified phase spectrum. This modification results into cancellation of low energy components (noise)...

OBJECTIVE The purpose was to determine if speech-recognition performances were the same when the speech level was fixed and the noise level varied as when the noise level was fixed and the speech level varied. DESIGN A descriptive/quasi-experimental experiment was conducted with Lists 3 and 4 of the revised speech perception in noise (R-SPIN) test, which involves high predictability (HP) and ...

The fixed dimension modified sinusoidal model (FDMSM) was recently proposed as an attractive candidate for compact representation of audio signals in adverse conditions. This paper aims to study the capability of the FDMSM signal representation for analysis and synthesis of speech mixtures as well as noisy audio signals corrupted by highly colored noise of babble and harmonic. Extensive simulat...

Background environmental noises degrade the performance of speech-processing systems (e.g. speech coding, speech recognition). By modifying the processing according to the type of background noise, the performance can be enhanced. This requires noise classification. In this work, four pattern-recognition frameworks have been used to design noise classification algorithms. Classification is done...

Non-native listeners’ perception of speech has been shown to be more adversely affected by interference from background noise than native speakers. This study focused on effects of different levels of linguistic information on speech perception in noise. We examined roles of phonetic and contextual cues in sentence perception in babble noise by Chinese learners and native speakers of English. O...

A novel method to reduce additive non-stationary noise is proposed. The proposed method requires neither the statistical assumption about noise nor the estimate of the noise statistics from any pause regions. The enhancement is performed on a band-by-band basis for each time frame. Based on both the decision on whether a particular band in a frame is speech or noise dominant and the masking pro...