Robust Speech Detection for Noisy Environments

This paper presents a robust voice activity detector (VAD) based on hidden Markov models (HMM) to improve speech recognition systems in stationary and non-stationary noise environments: inside motor vehicles (like cars or planes) or inside buildings close to high traffic places (like in a control tower for air traffic control (ATC)). In these environments, there is a high stationary noise level caused by vehicle motors and additionally, there could be people speaking at certain distance from the main speaker producing non-stationary noise. The VAD presented in this paper is characterized by a new front-end and a noise level adaptation process that increases significantly the VAD robustness for different signal to noise ratios (SNRs). The feature vector used by the VAD includes the most relevant Mel Frequency Cepstral Coefficients (MFCC), normalized log energy and delta log energy. The proposed VAD has been evaluated and compared to other well-known VADs using three databases containing different noise conditions: speech in clean environments (SNRs mayor que 20 dB), speech recorded in stationary noise environments (inside or close to motor vehicles), and finally, speech in non stationary environments (including noise from bars, television and far-field speakers). In the three cases, the detection error obtained with the proposed VAD is the lowest for all SNRs compared to Acero¿s VAD (reference of this work) and other well-known VADs like AMR, AURORA or G729 annex b

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

Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

Design of automatic speech recognition in noisy environments enhancement and modification

Recurrent neural networks (RNN) and feed-forward multi-layer perceptron’s have been proposed for determining the absence and presence of speech in continuous voice signals when there is a variety of background noise levels present. The Aurora2 and Aurora3 were used to conduct detailed performance evaluations on vocal activity detection. When a Recurrent neural network feeds on automatic speech recognition particular features and acoustic features, the best outcomes can be achieved, according to this study. Aurora2 and the French, Romanian and Norway portions of the Aurora3 corpus are also proposed for detailed studies of ASR. When noise presence probability is utilized to change for encoding speech, phone subsequent probabilities are employed; the WER is reduced by 10.3 percent

Anti-spoofing Methods for Automatic SpeakerVerification System

Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks. Inthis work we overview different acoustic feature spaces and classifiersto determine reliable and robust countermeasures against spoofing at-tacks. We compared several spoofing detection systems, presented so far,on the development and evaluation datasets of the Automatic SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge 2015.Experimental results presented in this paper demonstrate that the useof magnitude and phase information combination provides a substantialinput into the efficiency of the spoofing detection systems. Also wavelet-based features show impressive results in terms of equal error rate. Inour overview we compare spoofing performance for systems based on dif-ferent classifiers. Comparison results demonstrate that the linear SVMclassifier outperforms the conventional GMM approach. However, manyresearchers inspired by the great success of deep neural networks (DNN)approaches in the automatic speech recognition, applied DNN in thespoofing detection task and obtained quite low EER for known and un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer and Information Science (CCIS) vol. 66