|
|
- 2018
基于DNN-LSTM的VAD算法
|
Abstract:
基于深度神经网络（deep neural network，DNN）的语音活动性检测（voice activity detection，VAD）忽略了声学特征在时间上的相关性，在带噪环境下性能会明显下降。该文提出了一种基于深度神经网络和长短时记忆单元（long-short term memory，LSTM）的混合网络结构应用于VAD问题。进一步对语音帧的动态信息加以分析利用，同时结合DNN-LSTM结构使用一种基于上下文信息的代价函数用于网络训练。实验语料基于TIDIGITS语音库，使用Noisex-92噪声库加噪。实验结果表明：在不同噪声环境下基于DNN-LSTM的VAD方法比基于DNN的VAD方法性能更好，新的代价函数比传统的代价函数更适用于该文提出的算法。
Abstract：Voice activity detection (VAD) algorithms based on deep neural networks (DNN) ignore the temporal correlation of the acoustic features between speech frames which significantly reduces the performance in noisy environments. This paper presents a hybrid deep neural network with long-short term memory (LSTM) for VAD analyses which utilizes dynamic information from the speech frames. A context information based cost function is used to train the DNN-LSTM network. The noisy speech corpus used here was based on TIDIGITS and Noisex-92. The results show that the DNN-LSTM based VAD algorithm has better recognition accuracy than DNN-based VAD algorithms in noisy environment which shows that this cost function is more suitable than the traditional cost function.
| [1] | GRAVES A. Supervised sequence labelling with recurrent neural networks[M]. Berlin, Germany:Springer-Verlag, 2012. |
| [2] | COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(1):2493-2537. |
| [3] | DUCHI J, HAZAN E, SINGER Y. Adaptive subgradient methods for online learning and stochastic optimization[J]. Journal of Machine Learning Research, 2011, 12(7):2121-2159. |
| [4] | SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:A simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958. |
| [5] | PEARCE D, HIRSCH H G. The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions[C]//The Proceedings of the 6th International Conference on Spoken Language Processing (Volume Ⅳ). Beijing, China:Interspeech, 2000:29-32. |
| [6] | SHAO Y, JIN Z, WANG D L, et al. An auditory-based feature for robust speech recognition[C]//International Conference on Acoustics, Speech and Signal Processing. Taipei, China:IEEE, 2009:4625-4628. |
| [7] | HE K, ZHANG X, REN S, et al. Delving deep into rectifiers:surpassing human-level performance on imagenet classification[C]//IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1026-1034. |
| [8] | GHOSH P K, TSIARTAS A, NARAYANAN S. Robust voice activity detection using long-term signal variability[J]. IEEE Transactions on Audio Speech & Language Processing, 2011, 19(3):600-613. |
| [9] | SALISHEV S, BARABANOV A, KOCHAROV D, et al. Voice activity detector (VAD) based on long-term Mel frequency band features[C]//International Conference on Text, Speech, and Dialogue. Brno, Czech Republic:Springer International Publishing, 2016:352-358. |
| [10] | ZHOU Q, MA L, ZHENG Z, et al. Recurrent neural word segmentation with tag inference[M]. Kunming, China:Natural Language Understanding and Intelligent Applications Springer International Publishing, 2016. |
| [11] | BENYASSINE A, SHLOMOT E, SU H Y, et al. A robust low complexity voice activity detection algorithm for speech communication systems[C]//Speech Coding for Telecommunications Proceeding. Pocono Manor, USA:IEEE, 1997:97-98. |
| [12] | CHO N, KIM E K. Enhanced voice activity detection using acoustic event detection and classification[J]. IEEE Transactions on Consumer Electronics, 2011, 57(1):196-202. |
| [13] | CHANG J H, KIM N S. Voice activity detection based on complex Laplacian model[J]. Electronics Letters, 2003, 39(7):632-634. |
| [14] | RAMIREZ J, YELAMOS P, GORRIZ J M, et al. SVM-based speech endpoint detection using contextual speech features[J]. Institution of Engineering and Technology, 2006, 42(7):426-428. |
| [15] | ZHANG X L, WU J. Deep belief network based voice activity detection[J]. Audio, Speech, and Language Processing, 2013, 21(4):691-710. |
| [16] | HAS,IM SAK, SENIOR A, RAO K, et al. Learning acoustic frame labeling for speech recognition with recurrent neuralnetworks[C]//International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia:IEEE, 2015:4280-4284. |
| [17] | ZHANG X L, WANG D. Boosted deep neural networks and multi-resolution cochleagram features for voice activity detection[J]. Speech and Signal Processing, 2014:6645-6649. |
| [18] | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 2012, 9(8):1735-1780. |
