To determine speech intelligibility using the test suggested by Ozimek et al. (2009), the subject composed sentences with the words presented on a computer screen. However, the number and the type of these words were chosen arbitrarily. The subject was always presented with 18, similarly sounding words. Therefore, the aim of this study was to determine whether the number and the type of alternative words used by Ozimek et al. (2009), had a significant influence on the speech intelligibility. The aim was also to determine an optimal number of alternative words: i.e., the number that did not affect the speech reception threshold (SRT) and not unduly lengthened the duration of the test. The study conducted using a group of 10 subjects with normal hearing showed that an increase in the number of words to choose from 12 to 30 increased the speech intelligibility by about 0.3 dB/6 words. The use of paronyms as alternative words as opposed to random words, leads to an increase in the speech intelligibility by about 0.6 dB, which is equivalent to a decrease in intelligibility by 15 percentage points. Enlarging the number of words to choose from, and switching alternative words to paronyms, led to an increase in response time from approximately 11 to 16 s. It seems that the use of paronyms as alternative words as well as using 12 or 18 words to choose from is the best choice when using the Polish Sentence Test (PST).
This study sought to evaluate the effect of speech intensity on performance of the Callsign Acquisition Test (CAT) and Modified Rhyme Test (MRT) presented in noise. Fourteen normally hearing listeners performed both tests in 65 dB A white background noise. Speech intensity varied while background noise remained constant to form speech-to-noise ratios (SNRs) of -18, -15, -12, -9, and -6 dB. Results showed that CAT recognition scores were significantly higher than MRT scores at the same SNRs; however, the scores from both tests were highly correlated and their relationship for the SNRs tested can be expressed by a simple linear function. The concept of CAT can be easily ported to other languages for testing speech communication under adverse listening conditions.
Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.
The paper presents the results of sentence and logatome speech intelligibility measured in rooms with induction loop for hearing aid users. Two rooms with different acoustic parameters were chosen. Twenty two subjects with mild, moderate and severe hearing impairment using hearing aids took part in the experiment. The intelligibility tests composed of sentences or logatomes were presented to the subjects at fixed measurement points of an enclosure. It was shown that a sentence test is more useful tool for speech intelligibility measurements in a room than logatome test. It was also shown that induction loop is very efficient system at improving speech intelligibility. Additionally, the questionnaire data showed that induction loop, apart from improving speech intelligibility, increased a subject’s general satisfaction with speech perception
The aim of this work was to measure subjective speech intelligibility in an enclosure with a long reverberation time and comparison of these results with objective parameters. Impulse Responses (IRs) were first determined with a dummy head in different measurement points of the enclosure. The following objective parameters were calculated with Dirac 4.1 software: Reverberation Time (RT), Early Decay Time (EDT), weighted Clarity (C50) and Speech Transmission Index (STI). For the chosen measurement points, a convolution of the IRs with the Polish Sentence Test (PST) and logatome tests was made. PST was presented at a background of a babble noise and speech reception threshold - SRT (i.e. SNR yielding 50% speech intelligibility) for those points were evaluated. A relationship of the sentence and logatome recognition vs. STI was determined. It was found that the final SRT data are well correlated with speech transmission index (STI), and can be expressed by a psychometric function. The difference between SRT determined in condition without reverberation and in reverberation conditions appeared to be a good measure of the effect of reverberation on speech intelligibility in a room. In addition, speech intelligibility, with and without use of the sound amplification system installed in the enclosure, was compared.