In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi textindependent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.

A: Deep Speaker Vectors for Semi Text-independent Speaker Verification
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Q: Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit's effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.

A: Training RNNs as Fast as CNNs
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Q: Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for<lb>complex numbers) information. Over the last two decades, a popular generic empirical approach<lb>to the many variants of this problem has been one of alternating minimization; i.e. alternating<lb>between estimating the missing phase information, and the candidate solution. In this paper, we<lb>show that a simple alternating minimization algorithm geometrically converges to the solution<lb>of one such problem finding a vector x from y,A, where y = |Ax| and |z| denotes a vector<lb>of element-wise magnitudes of z under the assumption that A is Gaussian.<lb>Empirically, our algorithm performs similar to recently proposed convex techniques for this<lb>variant (which are based on "lifting" to a convex matrix problem) in sample complexity and<lb>robustness to noise. However, our algorithm is much more efficient and can scale to large<lb>problems. Analytically, we show geometric convergence to the solution, and sample complexity<lb>that is off by log factors from obvious lower bounds. We also establish close to optimal scaling<lb>for the case when the unknown vector is sparse. Our work represents the only known theoretical<lb>guarantee for alternating minimization for any variant of phase retrieval problems in the non-<lb>convex setting.

A:
Phase Retrieval using Alternating Minimization
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