23,825 research outputs found
MFCC AND CMN BASED SPEAKER RECOGNITION IN NOISY ENVIRONMENT
The performance of automatic speaker recognition (ASR) system degrades drastically in the presence of noise and other distortions, especially when there is a noise level mismatch between the training and testing environments. This paper explores the problem of speaker recognition in noisy conditions, assuming that speech signals are corrupted by noise. A major problem of most speaker recognition systems is their unsatisfactory performance in noisy environments. In this experimental research, we have studied a combination of Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Cepstral Mean Normalization (CMN) techniques for speech enhancement. Our system uses a Gaussian Mixture Models (GMM) classifier and is implemented under MATLAB®7 programming environment. The process involves the use of speaker data for both training and testing. The data used for testing is matched up against a speaker model, which is trained with the training data using GMM modeling. Finally, experiments are carried out to test the new model for ASR given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system
LibriMix: An Open-Source Dataset for Generalizable Speech Separation
In recent years, wsj0-2mix has become the reference dataset for
single-channel speech separation. Most deep learning-based speech separation
models today are benchmarked on it. However, recent studies have shown
important performance drops when models trained on wsj0-2mix are evaluated on
other, similar datasets. To address this generalization issue, we created
LibriMix, an open-source alternative to wsj0-2mix, and to its noisy extension,
WHAM!. Based on LibriSpeech, LibriMix consists of two- or three-speaker
mixtures combined with ambient noise samples from WHAM!. Using Conv-TasNet, we
achieve competitive performance on all LibriMix versions. In order to fairly
evaluate across datasets, we introduce a third test set based on VCTK for
speech and WHAM! for noise. Our experiments show that the generalization error
is smaller for models trained with LibriMix than with WHAM!, in both clean and
noisy conditions. Aiming towards evaluation in more realistic,
conversation-like scenarios, we also release a sparsely overlapping version of
LibriMix's test set.Comment: submitted to INTERSPEECH 202
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
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