• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.121-126
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• 2012

This study deals with how stuttering changes in its frequency in a situation where adult participants who stutter are exposed to one type of background noise, that is, multi-talker babble noise. Eight American English-speaking adults who stutter participated in this study. Each of the subjects read aloud sentences under each of three speaking conditions (i.e., typical solo reading (TSR), typical choral reading (TCR), and multi-talker babble noise reading (BNR)). Speech fluency was computed based on a percentage of syllables stuttered (%SS) and speaking rate was also assessed to examine if there was significant change in rates as a measure of vocal change under each of the speaking conditions. The study found that participants read more fluently both during BNR and during TCR than during TSR. The study also found that participants did not show significant changes in speaking rate across the three speaking conditions. Some discussion was provided in relation to the effect of multi-talker babble noise on the frequency of stuttering and its further speculation.

• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.117-124
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• 2011

This study investigated the effects of the types of noise and signal to noise ratios (SNRs) on speech intelligibility of an adult with dysartrhia. Speech intelligibility was judged by 48 naive listeners using a word transcription task. Repeated measures design was used with the types of noise (multi-talker babble/environmental noise) and SNRs (0, +10 dB, +20 dB) as within-subject factors. The dependent measure was the percentage of correctly transcribed words. Results revealed that two main effects were statistically significant. Listeners performed significantly worse in the multi-talker babble condition than the environmental noise condition, and they performed significantly better at higher levels of SNRs. The current results suggested that the multi-talker babble and lower level of SNRs decreased the speech intelligibility of adults with dysarthria, and speech-language pathologists should consider environmental factors such as the types of noise and SNRs in evaluating speech intelligibility of adults with dysarthria.

• Journal of Audiology & Otology
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• v.23 no.4
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• pp.181-186
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• 2019

Background and Objectives: This study compared the perception of mono-syllabic and bisyllabic words in Tamil by young normal hearing adults in the presence of multi-talker speech babble at two signal-to-noise ratios (SNRs). Further for this comparison, a speech perception in noise test was constructed using existing mono-syllabic and bi-syllabic word lists in Tamil. Subjects and Methods: A total of 30 participants with normal hearing in the age range of 18 to 25 years participated in the study. Speech-in-noise test in Tamil (SPIN-T) constructed using mono-syllabic and bi-syllabic words in Tamil was used as stimuli. The stimuli were presented in the background of multi-talker speech babble at two SNRs (0 dB and +10 dB SNR). Results: The effect of noise on SPIN-T varied with SNR. All the participants performed better at +10 dB SNR, the higher of the two SNRs considered. Additionally, at +10 dB SNR performance did not vary significantly for neither mono-syllabic or bi-syllabic words. However, a significant difference existed at 0 dB SNR. Conclusions: The current study indicated that higher SNR leads to better performance. In addition, bi-syllabic words were identified with minimal errors compared to mono-syllabic words. Spectral cues were the most affected in the presence of noise leading to more of place of articulation errors for both mono-syllabic and bi-syllabic words.

• Korean Journal of Audiology
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• v.23 no.4
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• pp.181-186
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• 2019

Background and Objectives: This study compared the perception of mono-syllabic and bisyllabic words in Tamil by young normal hearing adults in the presence of multi-talker speech babble at two signal-to-noise ratios (SNRs). Further for this comparison, a speech perception in noise test was constructed using existing mono-syllabic and bi-syllabic word lists in Tamil. Subjects and Methods: A total of 30 participants with normal hearing in the age range of 18 to 25 years participated in the study. Speech-in-noise test in Tamil (SPIN-T) constructed using mono-syllabic and bi-syllabic words in Tamil was used as stimuli. The stimuli were presented in the background of multi-talker speech babble at two SNRs (0 dB and +10 dB SNR). Results: The effect of noise on SPIN-T varied with SNR. All the participants performed better at +10 dB SNR, the higher of the two SNRs considered. Additionally, at +10 dB SNR performance did not vary significantly for neither mono-syllabic or bi-syllabic words. However, a significant difference existed at 0 dB SNR. Conclusions: The current study indicated that higher SNR leads to better performance. In addition, bi-syllabic words were identified with minimal errors compared to mono-syllabic words. Spectral cues were the most affected in the presence of noise leading to more of place of articulation errors for both mono-syllabic and bi-syllabic words.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1769-1776
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• 2013

In this paper, we proposed the modified YL(Yi and Loizou) algorithm, using a VAD(voice activity detector) for enhancing speech corrupted by colored noise. The performance of the proposed algorithm has been compared to the YL algorithm and LS(Lee and Son, etc.) algorithm by computer simulation. The colored noises used in the experiment were a car noise and multi-talker babble from the AURORA data base and the used voices from the TIMIT data base. It is confirmed that the proposed algorithm shows better performance from SNR(signal to noise ratio) and SSD(speech spectral distortion) viewpoint over the previous two approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1665-1674
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• 2011

In this paper, we proposed an algorithm for speech enhancement of speeches corrupted by colored noise. When there is no correlation between colored noise and speech signal, the colored noise turns into white noise through whitening transformation. This transformed signal has been applied to the generalized subspace approach for speech enhancement. The speech spectral distortion, produced by the whitening transformation as pre-processing, has been restored by using the inverse whitening transformation as post-processing of the proposed algorithm. The performance of the proposed algorithm for speech enhancement has been confirmed by computer simulation. The colored noises used in this experiment were car noise and multi-talker babble. It is confirmed that the proposed algorithm shows better performance from SNR and SSD viewpoint over the previous approach with the data from the AURORA and TIMIT data base.