Chinese speech intelligibility at different speech sound pressure levels and signal-to-noise ratios in simulated classrooms

The speech intelligibility in classroom can be influenced by background-noise levels, speech sound pressure level (SSPL), reverberation time and signal-to-noise ratio (SNR). The relationship between SSPL and subjective Chinese Mandarin speech intelligibility and the effect of different SNRs on Chinese Mandarin speech intelligibility in the simulated classroom were investigated through room acoustical simulation, auralisation technique and subjective evaluation. Chinese speech intelligibility test signals recorded in anechoic chamber were convolved with the simulated binaural room impulse responses, and then reproduced through the headphone by different SSPLs and SNRs. The results show that Chinese Mandarin speech intelligibility scores increase with increasing of SSPLs and SNRs within a certain range in simulated classrooms. Chinese Mandarin speech intelligibility scores have no significant difference with SNRs of no less than 15 dBA under the same reverberation time condition.     

A new sound coding strategy for suppressing noise in cochlear implants

In the n-of-m strategy, the signal is processed through m bandpass filters from which only the n maximum envelope amplitudes are selected for stimulation. While this maximum selection criterion, adopted in the advanced combination encoder strategy, works well in quiet, it can be problematic in noise as it is sensitive to the spectral composition of the input signal and does not account for situations in which the masker completely dominates the target. A new selection criterion is proposed based on the signal-to-noise ratio (SNR) of individual channels. The new criterion selects target-dominated (SNR > or = 0 dB) channels and discards masker-dominated (SNR<0 dB) channels. Experiment 1 assessed cochlear implant users' performance with the proposed strategy assuming that the channel SNRs are known. Results indicated that the proposed strategy can restore speech intelligibility to the level attained in quiet independent of the type of masker (babble or continuous noise) and SNR level (0-10 dB) used. Results from experiment 2 showed that a 25% error rate can be tolerated in channel selection without compromising speech intelligibility. Overall, the findings from the present study suggest that the SNR criterion is an effective selection criterion for n-of-m strategies with the potential of restoring speech intelligibility.     

Dual-channel spectral subtraction algorithms based speech enhancement dedicated to a bilateral cochlear implant

In this paper, two speech enhancement algorithms (SEAs) based on spectral subtraction (SS) principle have been evaluated for bilateral cochlear implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using power spectral densities (PSD) and cross power spectral density (CPSD) of the observed signals was studied. The enhanced speech signals were obtained using either Dual Channel Non Linear Spectral Subtraction ‘DC-NLSS’ or Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithms. For performance evaluation, some objective speech assessment tests relying on Perceptual Evaluation of Speech Quality (PESQ) score and speech Itakura-Saito (IS) distortion measurement were performed to fix the optimal number of frequency band needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests were assessed with 50 normal hearing listeners using a specific BCI simulator and with three deafened BCI patients. Experimental results, obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR), showed that DC-MBSS algorithm improves speech understanding better than DC-NLSS algorithm for single and multiple interfering noise sources.     

Envelope expansion methods for speech enhancement

The last decade has seen increasing interest in techniques for the enhancement of digital speech signals. Significant gains have been made in terms of signal-to-noise ratio (SNR) and quality, but few techniques have produced improvements in intelligibility. A method for speech enhancement based on nonlinear expansion of the spectral envelope is presented. The expansion is consistent with both the long-term spectrum of the speech and with the probability that speech is present in a given sample. Objective SNR measures are used to compare this algorithm with the well-known spectral subtraction method, with an alternative expansion scheme, and with limiting SNRs resulting from perfect recovery of the amplitude spectrum. For the purpose of intelligibility assessments, a simplified version of the algorithm has been implemented on a Texas Instruments TMS320-C25 system. Listening trials with this real-time system, conducted using a modified rhyme test, have produced small, but consistent, improvements in articulation scores.     

A comparative intelligibility study of single-microphone noise reduction algorithms

The evaluation of intelligibility of noise reduction algorithms is reported. IEEE sentences and consonants were corrupted by four types of noise including babble, car, street and train at two signal-to-noise ratio levels (0 and 5 dB), and then processed by eight speech enhancement methods encompassing four classes of algorithms: spectral subtractive, sub-space, statistical model based and Wiener-type algorithms. The enhanced speech was presented to normal-hearing listeners for identification. With the exception of a single noise condition, no algorithm produced significant improvements in speech intelligibility. Information transmission analysis of the consonant confusion matrices indicated that no algorithm improved significantly the place feature score, significantly, which is critically important for speech recognition. The algorithms which were found in previous studies to perform the best in terms of overall quality, were not the same algorithms that performed the best in terms of speech intelligibility. The subspace algorithm, for instance, was previously found to perform the worst in terms of overall quality, but performed well in the present study in terms of preserving speech intelligibility. Overall, the analysis of consonant confusion matrices suggests that in order for noise reduction algorithms to improve speech intelligibility, they need to improve the place and manner feature scores.     

The effects of compression ratio, signal-to-noise ratio, and level on speech recognition in normal-hearing listeners.

Previous research has demonstrated reduced speech recognition when speech is presented at higher-than-normal levels (e.g., above conversational speech levels), particularly in the presence of speech-shaped background noise. Persons with hearing loss frequently listen to speech-in-noise at these levels through hearing aids, which incorporate multiple-channel, wide dynamic range compression. This study examined the interactive effects of signal-to-noise ratio (SNR), speech presentation level, and compression ratio on consonant recognition in noise. Nine subjects with normal hearing identified CV and VC nonsense syllables in a speech-shaped noise at two SNRs (0 and +6 dB), three presentation levels (65, 80, and 95 dB SPL) and four compression ratios (1:1, 2:1, 4:1, and 6:1). Stimuli were processed through a simulated three-channel, fast-acting, wide dynamic range compression hearing aid. Consonant recognition performance decreased as compression ratio increased and presentation level increased. Interaction effects were noted between SNR and compression ratio, as well as between presentation level and compression ratio. Performance decrements due to increases in compression ratio were larger at the better (+6 dB) SNR and at the lowest (65 dB SPL) presentation level. At higher levels (95 dB SPL), such as those experienced by persons with hearing loss, increasing compression ratio did not significantly affect speech intelligibility.     

Stochastic resonance in an under-damped bistable system driven by harmonic mixing signal

Stochastic resonance(SR) is studied in an under-damped bistable system driven by the harmonic mixing signal and Gaussian white noise. Using the linear response theory(LRT), the expressions of the spectral amplification at fundamental and higher-order harmonic are obtained. The effects of damping coefficient, noise intensity, signal amplitude, and frequency on spectral amplifications are explored. Meanwhile, the power spectral density(PSD) and signal-to-noise ratio(SNR) are calculated to quantify SR and verify the theoretical results. The SNRs at the first and second harmonics exhibit a minimum first and a maximum later with increasing noise intensity. That is, both of the noise-induced suppression and resonance can be observed by choosing proper system parameters. Especially, when the ratio of the second harmonic amplitude to the fundamental one takes a large value, the SNR at the fundamental harmonic is a monotonic function of noise intensity and the SR phenomenon disappears.     

Speech signal modification to increase intelligibility in noisy environments

The role of transient speech components on speech intelligibility was investigated. Speech was decomposed into two components--quasi-steady-state (QSS) and transient--using a set of time-varying filters whose center frequencies and bandwidths were controlled to identify the strongest formant components in speech. The relative energy and intelligibility of the QSS and transient components were compared to original speech. Most of the speech energy was in the QSS component, but this component had low intelligibility. The transient component had much lower energy but was almost as intelligible as the original speech, suggesting that the transient component included speech elements important to speech perception. A modified version of speech was produced by amplifying the transient component and recombining it with the original speech. The intelligibility of the modified speech in background noise was compared to that of the original speech, using a psychoacoustic procedure based on the modified rhyme protocol. Word recognition rates for the modified speech were significantly higher at low signal-to-noise ratios (SNRs), with minimal effect on intelligibility at higher SNRs. These results suggest that amplification of transient information may improve the intelligibility of speech in noise and that this improvement is more effective in severe noise conditions.     

Contributions of cochlea-scaled entropy and consonant-vowel boundaries to prediction of speech intelligibility in noise

Recent evidence suggests that spectral change, as measured by cochlea-scaled entropy (CSE), predicts speech intelligibility better than the information carried by vowels or consonants in sentences. Motivated by this finding, the present study investigates whether intelligibility indices implemented to include segments marked with significant spectral change better predict speech intelligibility in noise than measures that include all phonetic segments paying no attention to vowels/consonants or spectral change. The prediction of two intelligibility measures [normalized covariance measure (NCM), coherence-based speech intelligibility index (CSII)] is investigated using three sentence-segmentation methods: relative root-mean-square (RMS) levels, CSE, and traditional phonetic segmentation of obstruents and sonorants. While the CSE method makes no distinction between spectral changes occurring within vowels/consonants, the RMS-level segmentation method places more emphasis on the vowel-consonant boundaries wherein the spectral change is often most prominent, and perhaps most robust, in the presence of noise. Higher correlation with intelligibility scores was obtained when including sentence segments containing a large number of consonant-vowel boundaries than when including segments with highest entropy or segments based on obstruent/sonorant classification. These data suggest that in the context of intelligibility measures the type of spectral change captured by the measure is important.     

光谱分辨率和信噪比对光谱线指数测量精度的影响

分析光谱分辨率和信噪比对19个Lick原子吸收线指数测量精度的影响。采用高斯卷积方法,将光谱变换到不同分辨率下,通过测量并比较光谱在不同分辨率下的线指数值探究分辨率改变对线指数测量精度的影响;通过向光谱叠加符合不同高斯分布的随机噪声方法,将无噪理论光谱改变到不同信噪下,通过测量并比较光谱在不同信噪比时的线指数值分析信噪比对线指数测量精度的影响。结果表明：改变光谱分辨率能够引起线指数测量值的变化,此变化程度依赖于分辨率的改变程度,且此依赖关系对不同指数呈不同变化趋势;信噪比对线指数测量精度的影响随信噪比的降低而增大,当信噪比高于25时,此影响可忽略。     

Atmospheric effects on voice command intelligibility from acoustic hail and warning devices

Voice command sound pressure levels (SPLs) were recorded at distances up to 1500 m. Received SPLs were related to the meteorological condition during sound propagation and compared with the outdoor sound propagation standard ISO 9613-2. Intelligibility of received signals was calculated using ANSI S3.5. Intelligibility results for the present voice command indicate that meteorological condition imposes little to no effect on intelligibility when the signal-to-noise ratio (SNR) is low (<-9 dB) or high (>0 dB). In these two cases the signal is firmly unintelligible or intelligible, respectively. However, at moderate SNRs, variations in received SPL can cause a fully intelligible voice command to become unintelligible, depending on the meteorological condition along the sound propagation path. These changes in voice command intelligibility often occur on time scales as short as minutes during upward refracting conditions, typically found above ground during the day or upwind of a sound source. Reliably predicting the intelligibility of a voice command in a moderate SNR environment can be challenging due to the inherent variability imposed by sound propagation through the atmosphere.     

The effect of overlap-masking on binaural reverberant word intelligibility

Reverberation interferes with the ability to understand speech in rooms. Overlap-masking explains this degradation by assuming reverberant phonemes endure in time and mask subsequent reverberant phonemes. Most listeners benefit from binaural listening when reverberation exists, indicating that the listener's binaural system processes the two channels to reduce the reverberation. This paper investigates the hypothesis that the binaural word intelligibility advantage found in reverberation is a result of binaural overlap-masking release with the reverberation acting as masking noise. The tests utilize phonetically balanced word lists (ANSI-S3.2 1989), that are presented diotically and binaurally with recorded reverberation and reverberation-like noise. A small room, 62 m3, reverberates the words. These are recorded using two microphones without additional noise sources. The reverberation-like noise is a modified form of these recordings and has a similar spectral content. It does not contain binaural localization cues due to a phase randomization procedure. Listening to the reverberant words binaurally improves the intelligibility by 6.0% over diotic listening. The binaural intelligibility advantage for reverberation-like noise is only 2.6%. This indicates that binaural overlap-masking release is insufficient to explain the entire binaural word intelligibility advantage in reverberation.     

一种抑制方向性噪声的双耳语音增强算法

为了给双耳听力设备佩戴者带来更好的语音可懂度,提出了一种利用双耳时间差与声级差的近场语音增强算法,该方法首先利用这两种差异来估计语音的功率谱和语音的相干函数,然后计算干扰噪声在左右耳间的头相关传输函数的比值,最后构造两个维纳滤波器。客观评价的参数显示该算法去噪效果优于对比算法而目标语音的时间差误差和声级差误差低于对比算法。主观的言语接受阈测试表明该方法能有效提高语音可懂度。结果表明,该算法在能够有效去除干扰噪声的同时,保留了目标语音的空间信息。      

Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing

A model for predicting the intelligibility of processed noisy speech is proposed. The speech-based envelope power spectrum model has a similar structure as the model of Ewert and Dau [(2000). J. Acoust. Soc. Am. 108, 1181-1196], developed to account for modulation detection and masking data. The model estimates the speech-to-noise envelope power ratio, SNR(env), at the output of a modulation filterbank and relates this metric to speech intelligibility using the concept of an ideal observer. Predictions were compared to data on the intelligibility of speech presented in stationary speech-shaped noise. The model was further tested in conditions with noisy speech subjected to reverberation and spectral subtraction. Good agreement between predictions and data was found in all cases. For spectral subtraction, an analysis of the model's internal representation of the stimuli revealed that the predicted decrease of intelligibility was caused by the estimated noise envelope power exceeding that of the speech. The classical concept of the speech transmission index fails in this condition. The results strongly suggest that the signal-to-noise ratio at the output of a modulation frequency selective process provides a key measure of speech intelligibility.     

光谱仪器信噪比与建模波长数对光谱分析精度的影响

为了研究光谱建模分析中光谱仪器噪声和参与建模的波长数两个因素与定量分析精度之间的关系,针对不同信噪比对模型精度的影响、参与建模的波长数与分析精度的关系、不同信噪比波段中多波长参与建模对分析精度的影响三个方面进行了理想样本建模分析。实验结果表明：光谱测量仪器的噪声水平直接影响建模分析误差,而使用多波长尤其是信噪比较好的波段的光谱参与线性建模,近似满足波长数每增加4倍,分析精度提高2倍的关系,能够在一定程度上弥补信噪比的不足。研究为在光谱分析中充分利用和提高光谱仪器信噪比、合理选择参与建模波长数及波段,从而提高光谱定量分析精度提供了实验基础和理论指导。     

Intelligibility enhancement for noisy whispered speech using asymmetric cost function

We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech distinctively.The modified Itakura-Saito（MIS）distance function provides more penalties to speech amplification distortion,whereas the Kullback-Leibler（KL）divergence function gives more penalties to speech attenuation distortion.The experimental results show that the MIS function based method achieves significant improvement of intelligibility in contrast to the conventional speech enhancement algorithms when the signal-to-noise ratio（SNR）falls below-6 dB,whereas the KL function based one achieves the similar result as the minimum mean square error（MMSE）speech enhancement method.The results show that the effects of the amplification and attenuation distortions on the intelligibility of the enhanced whisper are different,where larger attenuation distortion may result in better intelligibility of speech with low SNR.However,the attenuation distortion has small effects on intelligibility of speech with high SNR.     

A detailed study on the effects of noise on speech intelligibility

A wavelet representation of speech was used to display the instantaneous amplitude and phase within 14 octave frequency bands, representing the envelope and the carrier within each band. Adding stationary noise alters the wavelet pattern, which can be understood as a combination of three simultaneously occurring subeffects: two effects on the wavelet levels (one systematic and one stochastic) and one effect on the wavelet phases. Specific types of signal processing were applied to speech, which allowed each effect to be either included or excluded. The impact of each effect (and of combinations) on speech intelligibility was measured with CVC's. It appeared that the systematic level effect (i.e., the increase of each speech wavelet intensity with the mean noise intensity) has the most degrading effect on speech intelligibility, which is in accordance with measures such as the modulation transfer function and the speech transmission index. However, also the introduction of stochastic level fluctuations and disturbance of the carrier phase seriously contribute to reduced intelligibility in noise. It is argued that these stochastic effects are responsible for the limited success of spectral subtraction as a means to improve speech intelligibility. Results can provide clues for effective noise suppression with respect to intelligibility.     

Effects of signal-to-noise ratio, signal periodicity, and degree of hearing impairment on the performance of voice-separation algorithms

Procedures for enhancing the intelligibility of a target talker in the presence of a co-channel competing talker were evaluated in tests involving (i) continuously voiced sentences spoken on a monotone, (ii) continuously voiced sentences with time-varying intonation, and (iii) noncontinuously voiced sentences produced with natural intonation. The procedures were based on the methods of harmonic selection and cepstral filtering [R.J. Stubbs and Q. Summerfield, J. Acoust. Soc. Am. 87, 359-372 (1990)]. Target and competing voices were combined at signal-to-noise ratios (SNRs) between -10 dB and +10 dB. Subjects were a group with normal hearing and a heterogeneous group with mild-moderate cochlear hearing impairments. Processing enhanced the target voice over a range of SNRs for each type of sentence and for most listeners. Enhancement was greatest at negative SNRs. Among the impaired listeners, benefit was generally greater for those with milder losses. These results consolidate and extend previous demonstrations that voice-separation algorithms that exploit the harmonic structure of the voiced portions of speech can enhance intelligibility. However, practical application of such algorithms depends on a solution to the problem of tracking the fundamental-frequency contour of one voice in the presence of a competing voice.     

A localization algorithm based on head-related transfer functions

Two sound localization algorithms based on the head-related transfer function were developed. Each of them uses the interaural time delay, interaural level difference, and monaural spectral cues to estimate the location of a sound source. Given that most localization algorithms will be required to function in background noise, the localization performance of one of the algorithms was tested at signal-to-noise ratios (SNRs) from 40 to -40 dB. Stimuli included ten real-world, broadband sounds located at 5 degrees intervals in azimuth and at 0 degrees elevation. Both two- and four-microphone versions of the algorithm were implemented to localize sounds to 5 degrees precision. The two-microphone version of the algorithm exhibited less than 2 degrees mean localization error at SNRs of 20 dB and greater, and the four-microphone version committed approximately 1 degrees mean error at SNRs of 10 dB or greater. Potential enhancements and applications of the algorithm are discussed.     

提高耳语音可懂度的非对称压缩语音增强方法

提出两种基于非对称代价函数的耳语音增强算法,将语音增强过程中的放大失真和压缩失真区分对待。Modified ItakuraSaito (MIS)算法对放大失真给予更多的惩罚,而Kullback-Leibler (KL)算法则对压缩失真给予更多的惩罚。实验结果表明,在低于—6 dB的低信噪比情况中,经MIS算法增强后的耳语音的可懂度相比传统算法有显著提高;而KL算法则获得了同最小均方误差语音增强算法近似的可懂度提高效果,证实了耳语音中的放大失真和压缩失真对于耳语音可懂度的影响并不相同,低信噪比时较大的压缩失真有助于提高耳语音可懂度,而高信噪比时的压缩失真对耳语音可懂度影响较小。