Open AccessPosted Content
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Greg Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Shubho Sengupta,Adam Coates,Andrew Y. Ng +10 more
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
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Abstract: We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.
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Citations
On training the recurrent neural network encoder-decoder for large vocabulary end-to-end speech recognition
Liang Lu,Xingxing Zhang,Steve Renais +2 more
- 20 Mar 2016
TL;DR: This paper presents a more effective stochastic gradient decent (SGD) learning rate schedule that can significantly improve the recognition accuracy, and demonstrates that using multiple recurrent layers in the encoder can reduce the word error rate.
A comparison of Deep Learning methods for environmental sound detection
Juncheng Li,Wei Dai,Florian Metze,Shuhui Qu,Samarjit Das +4 more
- 05 Mar 2017
TL;DR: This work presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes.
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Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
TL;DR: This paper conducts the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical black-box setting, and proposes an adversarial attack, named FakeBob, to craft adversarial samples.
Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks
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TL;DR: An acoustic-graphemic-phonemic model (AGPM) using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions (encoded as binary vectors), which develops a unified MDD framework which works much like free-phone recognition.
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Xiao Sun,Naigang Wang,Chia-Yu Chen,Jiamin Ni,Ankur Agrawal,Xiaodong Cui,Swagath Venkataramani,Kaoutar El Maghraoui,Vijayalakshmi Srinivasan,Kailash Gopalakrishnan +9 more
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TL;DR: A novel adaptive Gradient Scaling technique (GradScale) is explored that addresses the challenges of insufficient range and resolution in quantized gradients as well as explores the impact of quantization errors observed during model training.
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