Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Emotion Recognition from Physiological Signal Analysis: A Review

There is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tends to derive from sophisticated ranking functions, such as those constructed using learning to rank, while efficiency gains tend to arise from improvements in query evaluation and caching strategies. Given their inherently disjoint nature, it is difficult to jointly optimize effectiveness and efficiency in end-to-end systems. To address this problem, we formulate and develop a novel cascade ranking model, which unlike previous approaches, can simultaneously improve both top k ranked effectiveness and retrieval efficiency. The model constructs a cascade of increasingly complex ranking functions that progressively prunes and refines the set of candidate documents to minimize retrieval latency and maximize result set quality. We present a novel boosting algorithm for learning such cascades to directly optimize the tradeoff between effectiveness and efficiency. Experimental results show that our cascades are faster and return higher quality results than comparable ranking models.

/pdf/a-cascade-ranking-model-for-efficient-ranked-retrieval-4jorykxhcq.pdf

A cascade ranking model for efficient ranked retrieval

Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. We present a novel approach to this task that is based on measuring the standard deviation of retrieval scores in the result list of the documents most highly ranked. We argue that for retrieval methods that are based on document-query surface-level similarities, the standard deviation can serve as a surrogate for estimating the presumed amount of query drift in the result list, that is, the presence (and dominance) of aspects or topics not related to the query in documents in the list. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction quality often transcends that of current state-of-the-art prediction methods.

/pdf/predicting-query-performance-by-query-drift-estimation-4v2ecmkns1.pdf

Predicting Query Performance by Query-Drift Estimation

Speech is the most effective way for people to exchange complex information Recognition of emotional information contained in speech is one of the important challenges in the field of artificial intelligence To better acquire emotional features in speech signals, a parallelized convolutional recurrent neural network (PCRN) with spectral features is proposed for speech emotion recognition First, frame-level features are extracted from each utterance and, a long short-term memory is employed to learn these features frame by frame At the same time, the deltas and delta-deltas of the log Mel-spectrogram are calculated and reconstructed into three channels (static, delta, and delta-delta); these 3-D features are learned by a convolutional neural network (CNN) Then, the two learned high-level features are fused and batch normalized Finally, a SoftMax classifier is used to classify emotions Our PCRN model simultaneously processes two different types of features in parallel to better learn the subtle changes in emotion The experimental results on four public datasets show the superiority of our proposed method, which is better than the previous works

/pdf/parallelized-convolutional-recurrent-neural-network-with-27gjths7j9.pdf

Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition

Emotion recognition is an important factor of affective computing and has potential use in assistive technologies. In this paper we used landmark and other acoustic features to recognize different emotional states in speech. We analyzed 2442 utterances from the Emotional Prosody Speech and Transcripts corpus and extracted 62 features from each utterance. A neural network classifier was built to recognize different emotional states of these utterances. We obtained over 90% accuracy in distinguishing hot anger and neutral states, over 80% accuracy in distinguishing happy and sadness as well as in distinguishing hot anger and cold anger. We also achieved 62% and 49% accuracy for classifying 4 and 6 emotions respectively. We had 20% accuracy in classifying all 15 emotions in the corpus which is a large improvement over other studies. We plan to apply our work to developing a tool to help people who have difficulty in identifying emotion.

/pdf/recognizing-emotion-in-speech-using-neural-networks-41ub61x1s0.pdf

Recognizing emotion in speech using neural networks

When a user submits a query to a search engine, the search engine computes a score for each document according to its relevance to the query, and ranks the documents based on their scores. Due to the complexity of the modern search engine, the score itself is not sufficient for the information retrieval application requiring combining different ranked lists. Inferring the score distributions for relevant and non-relevant documents and estimating the probability of relevance become imperative. In this thesis, we address two major research questions: (1) How to model score distributions in a more accurate manner for relevant and non-relevant documents? (2) How can score distributions be better inferred in practice when the relevance information is absent? 
In the first part of the thesis, we show the existing problems of today's most widely used score distribution model, and propose to model the relevant document scores by a mixture of Gaussian distributions and the non-relevant scores by a Gamma distribution. Score distributions are further modeled in a more systematic manner. With a basic assumption of the distribution of terms in a document, the distribution of the produced scores for retrieved documents can be derived through the transformations applied on the term frequency. Meanwhile, the score distribution of relevant documents can also be derived through a general mathematical framework given the score distribution for all retrieved documents. 
The second part of the thesis presents a new framework for inferring score distributions when the relevance information is unavailable. The new inference process extends the expectation maximization algorithm by simultaneously considering the ranked lists of documents returned by multiple retrieval systems, and encodes the constraint that the same document retrieved by multiple systems should have the same, global, probability of relevance. Combined, we demonstrate that it is more effective when it is applied on the task of metasearch.

Modeling score distributions for information retrieval

Understanding the difference between emotions based on acoustic features is important for computer recognition and classification of emotions. We conducted a study of human perception of six emotions based on three perceptual dimensions and compared the human classification with machine classification based on many acoustic parameters. Results show that the six emotions cluster differently according to acoustic features and to perceptual dimensions. Acoustic features fail to characterize the perceptual dimension of valence. More research is needed to find acoustic features that have a close relation to human perception.

https://www.ccs.neu.edu/home/daikeshi/papers/src210-dai.pdf

Comparing emotions using acoustics and human perceptual dimensions

Inferring the distributions of relevant and nonrelevant documents over a ranked list of scored documents returned by a retrieval system has a broad range of applications including information filtering, recall-oriented retrieval, metasearch, and distributed IR. Typically, the distribution of documents over scores is modeled by a mixture of two distributions, one for the relevant and one for the nonrelevant documents, and expectation maximization (EM) is run to estimate the mixture parameters. A large volume of work has focused on selecting the appropriate form of the two distributions in the mixture. In this work we consider the form of the distributions as a given and we focus on the inference algorithm. We extend the EM algorithm (a) by simultaneously considering the ranked lists of documents returned by multiple retrieval systems, and (b) by encoding in the algorithm the constraint that the same document retrieved by multiple systems should have the same, global, probability of relevance. We test the new inference algorithm using TREC data and we demonstrate that it outperforms the regular EM algorithm. It is better calibrated in inferring the probability of document's relevance, and it is more effective when applied on the task of metasearch.

/pdf/extended-expectation-maximization-for-inferring-score-317ydz7i5t.pdf

Extended expectation maximization for inferring score distributions

: Ranking is a central problem in information retrieval. Modern search engines, especially those designed for the World Wide Web, commonly analyze and combine hundreds of features extracted from the submitted query and underlying documents in order to assess the relative relevance of a document to a given query and thus rank the underlying collection. The sheer size of this problem has led to the development of learning to rank (LTR) algorithms that can automate the construction of such ranking functions: Given a training set of (feature vector, relevance) pairs, a machine learning procedure learns how to combine the query and document features in such a way so as to effectively assess the relevance of any document to any query and thus rank a collection in response to a user input. Much thought and research has been placed on the development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on the construction of appropriate learning to rank data sets nor on the effect of these data sets on the ability of a learning-to-rank algorithm to "learn" effectively.

/pdf/northeastern-university-in-trec-2009-million-query-track-268thdu3z5.pdf

Northeastern University in TREC 2009 Million Query Track

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