448 research outputs found

    Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

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    Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

    On pre-image iterations for speech enhancement

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    In this paper, we apply kernel PCA for speech enhancement and derive pre-image iterations for speech enhancement. Both methods make use of a Gaussian kernel. The kernel variance serves as tuning parameter that has to be adapted according to the SNR and the desired degree of de-noising. We develop a method to derive a suitable value for the kernel variance from a noise estimate to adapt pre-image iterations to arbitrary SNRs. In experiments, we compare the performance of kernel PCA and pre-image iterations in terms of objective speech quality measures and automatic speech recognition. The speech data is corrupted by white and colored noise at 0, 5, 10, and 15 dB SNR. As a benchmark, we provide results of the generalized subspace method, of spectral subtraction, and of the minimum mean-square error log-spectral amplitude estimator. In terms of the scores of the PEASS (Perceptual Evaluation Methods for Audio Source Separation) toolbox, the proposed methods achieve a similar performance as the reference methods. The speech recognition experiments show that the utterances processed by pre-image iterations achieve a consistently better word recognition accuracy than the unprocessed noisy utterances and than the utterances processed by the generalized subspace method

    Assessing the effect of noise-reduction to the intelligibility of low-pass filtered speech

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    Given the fact that most hearing-impaired listeners have low-frequency residual hearing, the present work assessed the effect of applying commonly-used singlechannel noise-reduction (NR) algorithms to improve the intelligibility of low-pass filtered speech, which simulates the effect of understanding speech with low-frequency residual hearing of hearing-impaired patients. In addition, this study was performed with Mandarin speech, which is characterized by its significant contribution of information present in (low-frequency dominated) vowels to speech intelligibility. Mandarin sentences were corrupted by steady-state speech-shaped noise and processed by four types (i.e., subspace, statistical-modeling, spectral-subtractive, and Wiener-filtering) of single-channel NR algorithms. The processed sentences were played to normal-hearing listeners for recognition. Experimental results showed that existing single-channel NR algorithms were unable to improve the intelligibility of low-pass filtered Mandarin sentences. Wiener-filtering had the least negative influence to the intelligibility of low-pass filtered speech among the four types of single-channel NR algorithms examined

    Noise Reduction with Optimal Variable Span Linear Filters

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    Spectral Restoration Based Speech Enhancement for Robust Speaker Identification

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    Spectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms

    Low Rank and Sparsity Analysis Applied to Speech Enhancement via Online Estimated Dictionary

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    In this letter, we propose an online estimated local dictionary based single-channel speech enhancement algorithm, which focuses on low-rank and sparse matrix decomposition. In the proposed algorithm, a noisy speech spectrogram can be decomposed into low-rank background noise components and an activation of the online speech dictionary, on which both low-rank and sparsity constraints are imposed. This decomposition takes the advantage of local estimated exemplar’s high expressiveness on speech components and also accommodates nonstationary background noise. The local dictionary can be obtained through estimating the speech presence probability (SPP) by applying expectation–maximal algorithm, in which a generalized Gamma prior for speech magnitude spectrum is used. The proposed algorithm is evaluated using signal-to-distortion ratio, and perceptual evaluation of speech quality. The results show that the proposed algorithm achieves significant improvements at various SNRs when compared to four other speech enhancement algorithms, including improved Karhunen–Loeve transform approach, SPP-based MMSE, nonnegative matrix factorization-based robust principal component analysis (RPCA), and RPCA
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