The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms

Dean, David; Sridharan, Sridha; Vogt, Robbie; Mason, Michael

doi:10.21437/interspeech.2010-774

Cited by 124 publications

(34 citation statements)

References 2 publications

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“…We evaluate the performance of the proposed model by using signals from the speech dataset Wall Street Journal (WSJ0) [21], and the noise databases QUT-NOISE [22] and DEMAND [23]. QUT-NOISE is used in constructing datasets of both training and evaluation using 4 noise types "cafe", "car", "home", and "street" recorded in unique locations.…”

Section: Datasetsmentioning

confidence: 99%

Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

Fang

Carbajal

Wermter

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNRs). To increase the robustness of the VAE, we propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. We evaluate our approach on real recordings of different noisy environments and acoustic conditions using two different noise datasets. We show that our proposed noise-aware VAE outperforms the standard VAE in terms of overall distortion without increasing the number of model parameters. At the same time, we demonstrate that our model is capable of generalizing to unseen noise conditions better than a supervised feedforward deep neural network (DNN). Furthermore, we demonstrate the robustness of the model performance to a reduction of the noisy-clean speech training data size.

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Section: Datasetsmentioning

confidence: 99%

Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

Fang

Carbajal

Wermter

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Background noises were selected from various public datasets according to different use case scenarios. Living room background noise (HOME-LIVINGB) from the QUT-NOISE Database [18], TV audios from the IberSpeech-RTVE Challenge [19], and music 2 and conversations 3 from free libraries.…”

Section: External Datamentioning

confidence: 99%

Speech Enhancement for Wake-Up-Word detection in Voice Assistants

Bonet¹,

Cámbara²,

López³

et al. 2021

Preprint

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Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very important task for voice assistants. A very common issue of voice assistants is that they get easily activated by background noise like music, TV or background speech that accidentally triggers the device. In this paper, we propose a Speech Enhancement (SE) model adapted to the task of WUW detection that aims at increasing the recognition rate and reducing the false alarms in the presence of these types of noises. The SE model is a fully-convolutional denoising auto-encoder at waveform level and is trained using a log-Mel Spectrogram and waveform reconstruction losses together with the BCE loss of a simple WUW classification network. A new database has been purposely prepared for the task of recognizing the WUW in challenging conditions containing negative samples that are very phonetically similar to the keyword. The database is extended with public databases and an exhaustive data augmentation to simulate different noises and environments. The results obtained by concatenating the SE with a simple and state-ofthe-art WUW detectors show that the SE does not have a negative impact on the recognition rate in quiet environments while increasing the performance in the presence of noise, especially when the SE and WUW detector are trained jointly end-to-end.

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“…Finally, in order to increase the robustness of the approach, we also trained the structure III model jointly with clean and noisy speech data. Four noise types 6 from the QUT-NOISE database [72] (noise types are different to the test data) are used to generate the 20 dB noisy training data, with the amount of the noisy data being one quarter of the clean data. As can be seen in Tab.…”

Section: ) Clean Conditionmentioning

confidence: 99%

Convolutional Neural Networks to Enhance Coded Speech

Zhao

Liu

Fingscheidt

2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors, is a challenging task. In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to legacy G.711, even better than uncoded speech with statistical significance. The source code for the cepstral domain approach to enhance G.711-coded speech is made available 1 .

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The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms

Cited by 124 publications

References 2 publications

Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

Speech Enhancement for Wake-Up-Word detection in Voice Assistants

Convolutional Neural Networks to Enhance Coded Speech

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