The purpose of this project was to develop a spoken word-recognition task that could be used clinically to evaluate recognition performance of individuals with hearing loss in a background noise. The test instrument incorporated monosyllabic words at seven levels over a 35-dB range presented in a background of "multi-talker" babble that was fixed in level. In Experiment 1, we established normat...

In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a ‘mapping’ between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using ful...

This paper proposes a new environmental noise classification using principal component analysis (PCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the identified noise specific acoustic model. The proposed model applies PCA to a set of noise features, and results from PCA are used by a pattern classifier for noi...

Ubiquitous speech processing involves listening to speech in ecological environments where noise is often present. The current study investigates the neural mechanisms involved in perceiving speech in noise using a sparse sampling fMRI method. Subjects were asked to match auditorily presented words with picture choices. The auditory stimuli were either presented in quiet or embedded in multi-ta...

The acceptance of background noise can be assessed by having participants select the maximum background noise level (BNL) to which they are willing to listen while following speech at their most comfortable listening level (MCL). The difference between the selected BNL and MCL is the acceptable noise level (ANL). Preliminary investigations have revealed large between-participant ANL differences...

This paper introduces a word boundary detection algorithm that works in a variety of noise conditions including what is commonly called the ‘cocktail party’ situation. The algorithm uses the direction of the signal as the main criterion for differentiating between desired-speech and background noise. To determine the signal direction the algorithm calculates estimates of the time delay between ...

The Minimal-Pair ABX (MP-ABX) paradigm has been proposed as a method for evaluating speech features for zeroresource/unsupervised speech technologies. We apply it in a phoneme discrimination task on the Articulation Index corpus to evaluate the resistance to noise of various speech features. In Experiment 1, we evaluate the robustness to additive noise at different signal-to-noise ratios, using...

This paper presents results of the comparison between speech produced in silence and speech in noise, also known as Lombard speech. A temporal filtering algorithm was developed which successfully removes the ambient noise from recordings of Lombard speech by locating and subtracting a recording of the noise performed in the same environment. The filtering algorithm yields overall noise attenuat...

Abstract -The paper presents a new speech enhancement approach for a single channel speech enhancement in a noise environment. In this method speech is mixed with real-world noises from babble, car and street environments. In this paper we proposed modified Kalman method for effective speech enhancement. The proposed method is compared to the traditional Spectral Subtraction (SS), Wiener Filter...

The English alphabet is a small but dificult vocabulary fo r speech recognition, with many fine phonetic distinctions, such as M / N and B/V. We use speakerindependent classification of isolated English letters t o evaluate the relative performance of the D F T , Perceptual Linear Predictive analysis, and the cochleagram auditory model. Feedforward neural network classifiers were trained using ...