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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

The organization of behavior: A neuropsychological theory

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An Introduction To The Event Related Potential Technique

Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types

The perception and production of non-native speech sounds is the key to learning a new language. The differences between the native and the target language sound systems cause learning problems, but orthographic conventions may also affect the learning process. We tested whether a misleading orthography in contrast to phonemic transcription affects the manner in which native Finns learn to produce a non-native speech sound embedded in a pseudo word context. After the two day training protocol, the subjects who, in addition to the acoustic stimulation, were exposed to transcription cues, significantly changed their non-native productions according to the target. In contrast, the subjects who trained with the orthographic stimuli, changed their productions away from the acoustic target and towards the visual one. This result suggests that visual information is of crucial importance in learning to modulate articulation according to the target language model.

/pdf/non-native-production-training-with-an-acoustic-model-and-3vwv87yk53.pdf

Non-native production training with an acoustic model and orthographic or transcription cues

In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional formant tracking methods typically adopt a two-stage estimate-and-track strategy wherein an initial set of formant candidates are estimated using short-time analysis (e.g., 10–50 ms), followed by a tracking stage based on dynamic programming or a linear state-space model. One of the main disadvantages of these approaches is that the tracking stage, however good it may be, cannot improve upon the formant estimation accuracy of the first stage. The proposed TVQCP method provides a single-stage formant tracking that combines the estimation and tracking stages into one. TVQCP analysis combines three approaches to improve formant estimation and tracking: (1) it uses temporally weighted quasi-closed-phase analysis to derive closed-phase estimates of the vocal tract with reduced interference from the excitation source, (2) it increases the residual sparsity by using the $L_1$ optimization and (3) it uses time-varying linear prediction analysis over long time windows (e.g., 100–200 ms) to impose a continuity constraint on the vocal tract model and hence on the formant trajectories. Formant tracking experiments with a wide variety of synthetic and natural speech signals show that the proposed TVQCP method performs better than conventional and popular formant tracking tools, such as Wavesurfer and Praat (based on dynamic programming), the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on deep neural networks trained in a supervised manner). Matlab scripts for the proposed method can be found at: https://github.com/njaygowda/ftrack

/pdf/time-varying-quasi-closed-phase-analysis-for-accurate-2if39oz64g.pdf

Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals

Robust formant detection using group delay function and stabilized weighted linear prediction

Second language (L2) learning is widely studied in adult learners. This study was conducted in order to see how auditory training affects elderly learners’ L2 speech production and whether linguistically oriented seniors can benefit more from training compared to seniors not showing special interest in foreign languages. Hence, we studied seniors recruited from language courses and seniors with other than L2 related interests. The two day study included listenand-repeat trainings and production tests. The trained words included vowels from which the three first formants and their standard deviations were measured and analysed. The results showed that formant values changed differently as a function of training in the two groups. Also, the standard deviations developed differently: only the linguistically oriented seniors showed a deviation decrease, indicating non-native learning. This study showed that learning to produce a non-native phoneme can be easier for elderly learners who show a general interest in languages.

/pdf/does-interest-in-language-learning-affect-the-non-native-15p9hetvs6.pdf

Paavo Alku

Papers

Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types

Non-native production training with an acoustic model and orthographic or transcription cues

Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals

Robust formant detection using group delay function and stabilized weighted linear prediction

Does interest in language learning affect the non-native phoneme production in elderly learners?