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

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

Digital processing of speech signals

Rules and Representations

https://users.aalto.fi/~jpohjala/publications/csl14stc.pdf

Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits

This paper presents a syllable-based approach to unsupervised pattern discovery from speech. By first segmenting speech into syllable-like units, the system is able to limit potential word onsets and offsets to a finite number of candidate locations. These syllable tokens are then described using a set of features and clustered into a finite number of syllable classes. Finally, recurring syllable sequences or individual classes are treated as word candidates. Feasibility of the approach is investigated on spontaneous American English and Tsonga language samples with promising results. We also present a new and simple, oscillator-based algorithm for efficient unsupervised syllabic segmentation.

Unsupervised word discovery from speech using automatic segmentation into syllable-like units

Phone segmentation in ASR is usually performed indirectly by Viterbi decoding of HMM output. Direct approaches also exist, e.g., blind speech segmentation algorithms. In either case, performance of automatic speech segmentation algorithms is often measured using automated evaluation algorithms and used to optimize a segmentation system’s performance. However, evaluation approaches reported in literature were found to be lacking. Also, we have determined that increases in phone boundary location detection rates are often due to increased over-segmentation levels and not to algorithmic improvements, i.e., by simply adding random boundaries a better hit-rate can be achieved when using current quality measures. Since established measures were found to be insensitive to this type of random boundary insertion, a new R-value quality measure is introduced that indicates how close a segmentation algorithm’s performance is to an ideal point of operation.

/pdf/an-improved-speech-segmentation-quality-measure-the-r-value-ofmghjbl1s.pdf

An Improved Speech Segmentation Quality Measure: the R-value

Modeling and prediction of temporal sequences is central to many signal processing and machine learning applications. Prediction based on sequence history is typically performed using parametric models, such as fixed-order Markov chains ( $n$ -grams), approximations of high-order Markov processes, such as mixed-order Markov models or mixtures of lagged bigram models, or with other machine learning techniques. This paper presents a method for sequence prediction based on sparse hyperdimensional coding of the sequence structure and describes how higher order temporal structures can be utilized in sparse coding in a balanced manner. The method is purely incremental, allowing real-time online learning and prediction with limited computational resources. Experiments with prediction of mobile phone use patterns, including the prediction of the next launched application, the next GPS location of the user, and the next artist played with the phone media player, reveal that the proposed method is able to capture the relevant variable-order structure from the sequences. In comparison with the $n$ -grams and the mixed-order Markov models, the sparse hyperdimensional predictor clearly outperforms its peers in terms of unweighted average recall and achieves an equal level of weighted average recall as the mixed-order Markov chain but without the batch training of the mixed-order model.

Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns

This work studies automatic recognition of paralinguistic properties of speech. The focus is on selection of the most useful acoustic features for three classification tasks: 1) recognition of autism spectrum developmental disorders from child speech, 2) classification of speech into different affective categories, and 3) recognizing the level of social conflict from speech. The feature selection is performed using a new variant of random subset sampling methods with k-nearest neighbors (kNN) as a classifier. The experiments show that the proposed system is able to learn a set of important features for each recognition task, clearly exceeding the performance of the same classifier using the original full feature set. However, some effects of overfitting the feature sets to finite data are also observed and discussed.

Okko Räsänen

Papers

Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits

Unsupervised word discovery from speech using automatic segmentation into syllable-like units

An Improved Speech Segmentation Quality Measure: the R-value

Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns

Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech