Automatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background... Show moreAutomatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background of a foreign speaker of English, using phonetically interpretable measurements. The production of the ten monophthongs of (American) English by Dutch, Mandarin Chinese and American speakers was used as a test case. Vowel formants F1 (corresponding to articulatory vowel height), F2 (capturing vowel backness and lip rounding) and vowel duration were extracted. Clearly different duration and patterning of the vowels in the vowel space were seen. Automatic classification of the speaker’s native language was 90 percent correct when all acoustic parameters were used as predictors. Language identification was slightly poorer when only formant data were used (85% correct) and substantially poorer – but much better than chance – when only vowel duration was used (60% correct). We conclude that vowel duration provides a weaker cue to foreign-accent identification in English than the spectral properties but that the combination of both information sources yields the best results. Show less
Automatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background... Show moreAutomatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background of a foreign speaker of English, using phonetically interpretable measurements. The production of the ten monophthongs of (American) English by Dutch, Mandarin Chinese and American speakers was used as a test case. Vowel formants F1 (corresponding to articulatory vowel height), F2 (capturing vowel backness and lip rounding) and vowel duration were extracted. Clearly different duration and patterning of the vowels in the vowel space were seen. Automatic classification of the speaker’s native language was 90 percent correct when all acoustic parameters were used as predictors. Language identification was slightly poorer when only formant data were used (85% correct) and substantially poorer – but much better than chance – when only vowel duration was used (60% correct). We conclude that vowel duration provides a weaker cue to foreign-accent identification in English than the spectral properties but that the combination of both information sources yields the best results. Show less
This tutorial-like presentation provides a survey of acoustical correlates of word and sentence stress, with emphasis on Germanic languages such as Dutch and English. It also reviews what is known... Show moreThis tutorial-like presentation provides a survey of acoustical correlates of word and sentence stress, with emphasis on Germanic languages such as Dutch and English. It also reviews what is known about the perceptual cue value of the acoustic correlates of stress, and show that highly reliable correlates are not necessarily strong perceptual cues, and conversely that the strongest perceptual cue (pitch change) is an unreliable correlate. Show less
