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.

Input: Consider Input: Besides spoken words, speech signals also carry information about speaker gender, age, and emotional state which can be used in a variety of speech analysis applications. In this paper, a divide and conquer strategy for ensemble classification has been proposed to recognize emotions in speech. Intrinsic hierarchy in emotions has been utilized to construct an emotions tree, which assisted in breaking down the emotion recognition task into smaller sub tasks. The proposed framework generates predictions in three phases. Firstly, emotions are detected in the input speech signal by classifying it as neutral or emotional. If the speech is classified as emotional, then in the second phase, it is further classified into positive and negative classes. Finally, individual positive or negative emotions are identified based on the outcomes of the previous stages. Several experiments have been performed on a widely used benchmark dataset. The proposed method was able to achieve improved recognition rates as compared to several other approaches.

Output: Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest

Input: Consider Input: In this work we consider the stochastic minimization of nonsmooth convex loss functions, a central problem in machine learning. We propose a novel algorithm called Accelerated Nonsmooth Stochastic Gradient Descent (ANSGD), which exploits the structure of common nonsmooth loss functions to achieve optimal convergence rates for a class of problems including SVMs. It is the first stochastic algorithm that can achieve the optimal O(1/t) rate for minimizing nonsmooth loss functions (with strong convexity). The fast rates are confirmed by empirical comparisons, in which ANSGD significantly outperforms previous subgradient descent algorithms including SGD.

Output: Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure

Input: Consider Input: <lb>This paper addresses the problem of ad hoc microphone array calibration where only partial<lb>information about the distances between microphones is available. We construct a matrix<lb>consisting of the pairwise distances and propose to estimate the missing entries based on a novel<lb>Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and<lb>projection onto the Euclidean distance space. This approach confines the recovered matrix to the<lb>EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of<lb>the calibration performance are obtained considering the random and locally structured missing<lb>entries as well as the measurement noise on the known distances. This study elucidates the links<lb>between the calibration error and the number of microphones along with the noise level and the<lb>ratio of missing distances. Thorough experiments on real data recordings and simulated setups<lb>are conducted to demonstrate these theoretical insights. A significant improvement is achieved<lb>by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art<lb>techniques for ad hoc microphone array calibration.
Output: Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees