Discriminative Phoneme Sequences Extraction for Non-Native Speaker's Origin Classification

TL;DR

This paper introduces an automated method to classify non-native speakers' origin by extracting discriminative phoneme sequences from speech, achieving high accuracy and reducing errors compared to previous techniques.

Contribution

The paper presents a novel approach for extracting discriminative phoneme sequences for non-native speaker origin classification, demonstrating high accuracy and error reduction.

Findings

Achieved 96.3% correct classification rate.

Discovered significant discriminative phoneme sequences.

Reduced classification errors compared to existing methods.

Abstract

In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakers' origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.