Data Mining Practical Machine Learning Tools and Techniques

The power of feedback.

https://repositorio.una.ac.cr/bitstream/11056/7949/1/priciplesoflanguajeslearningandtaching-jbogarin.pdf

Principles of language learning and teaching

1. Place animal in induction chamber and anesthetize the mouse and ensure sedation. 2. Once the animal is sedated, move it to a nose cone for hair removal using cream. Only apply cream to the area of the chest that will be utilized for imaging. Once the hair is removed, wipe area with wet gauze to ensure all hair is removed. 3. Move the animal to the imaging platform and tape its paws to the ECG lead plates and insert rectal probe. Body temperature should be maintained at 36-37°C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic.

人唾液天疱疮抗原的纯化

On Educational psychology: A cognitive view.

http://speech.ee.ntu.edu.tw/previous_version/Modeling_Strik_SC99.pdf

Modeling pronunciation variation for ASR

In this paper, we examine the relationship between pedagogy and technology in Computer Assisted Pronunciation Training (CAPT) courseware. First, we will analyse available literature on second language pronunciation teaching and learning in order to derive some general guidelines for effective training. Second, we will present an appraisal of various CAPT systems with a view to establishing whether they meet pedagogical requirements. In this respect, we will show that many commercial systems tend to prefer technological novelties to the detriment of pedagogical criteria that could benefit the learner more. While examining the limitations of today's technology, we will consider possible ways to deal with these shortcomings. Finally, we will combine the information thus gathered to suggest some recommendations for future CAPT.

The Pedagogy-Technology Interface in Computer Assisted Pronunciation Training

To determine whether expert fluency ratings of read speech can be predicted on the basis of automatically calculated temporal measures of speech quality, an experiment was conducted with read speech of 20 native and 60 non-native speakers of Dutch. The speech material was scored for fluency by nine experts and was then analyzed by means of an automatic speech recognizer in terms of quantitative measures such as speech rate, articulation rate, number and length of pauses, number of dysfluencies, mean length of runs, and phonation/time ratio. The results show that expert ratings of fluency in read speech are reliable (Cronbach's alpha varies between 0.90 and 0.96) and that these ratings can be predicted on the basis of quantitative measures: for six automatic measures the magnitude of the correlations with the fluency scores varies between 0.81 and 0.93. Rate of speech appears to be the best predictor: correlations vary between 0.90 and 0.93. Two other important determinants of reading fluency are the rate at which speakers articulate the sounds and the number of pauses they make. Apparently, rate of speech is such a good predictor of perceived fluency because it incorporates these two aspects.

/pdf/quantitative-assessment-of-second-language-learners-fluency-eb2awv5pzz.pdf

Quantitative assessment of second language learners’ fluency by means of automatic speech recognition technology

This paper describes two experiments aimed at exploring the relationship between objective properties of speech and perceived fluency in read and spontaneous speech. The aim is to determine whether such quantitative measures can be used to develop objective fluency tests. Fragments of read speech (Experiment 1) of 60 non-native speakers of Dutch and of spontaneous speech (Experiment 2) of another group of 57 non-native speakers of Dutch were scored for fluency by human raters and were analyzed by means of a continuous speech recognizer to calculate a number of objective measures of speech quality known to be related to perceived fluency. The results show that the objective measures investigated in this study can be employed to predict fluency ratings, but the predictive power of such measures is stronger for read speech than for spontaneous speech. Moreover, the adequacy of the variables to be employed appears to be dependent on the specific type of speech material investigated and the specific task performed by the speaker.

Helmer Strik

Papers

Modeling pronunciation variation for ASR

The Pedagogy-Technology Interface in Computer Assisted Pronunciation Training

Quantitative assessment of second language learners’ fluency by means of automatic speech recognition technology

Quantitative assessment of second language learners’ fluency: Comparisons between read and spontaneous speech

Comparing different approaches for automatic pronunciation error detection