Machine learning techniques for the identification of cues for stop place
نویسندگان
چکیده
This paper is situated in a long line of phonetic studies that seek to determine and qualify the acoustic cues humans use to identify stop place. The present study draws from a database of 1500 CV tokens of American English and their values for the acoustic features thought to be cues for stop place identification, including (1) VOT, (2) energy of the burst and release, (3) spectrum at the burst, and (4) formant transitions into the following vowel. Decision trees are used to determine the relative invariance of these acoustic features, which indicates their potential to serve as useful cues for listeners cross-contextually. Decision trees thus allow the evaluation of vocalic effects on this hierarchy of features for the purpose of guiding classic perceptual confusion studies.
منابع مشابه
Behavioral Analysis of Traffic Flow for an Effective Network Traffic Identification
Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملFault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...
متن کاملUsing Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media
Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine l...
متن کاملModeling of Chloride Ion Separation by Nanofiltration Using Machine Learning Techniques
In this work, several machine learning techniques are presented for nanofiltration modeling. According to the results, specific errors are defined. The rejection due to Nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. Methods of machine learning represent the rejection of nanofiltration as a function of concentration, pH, pressure and also ...
متن کامل