Cross-entropic comparison of formants of British, Australian and American English accents

Ghorshi, Seyed; Vaseghi, Saeed; Yan, Qing

doi:10.1016/j.specom.2008.03.013

Cited by 15 publications

(11 citation statements)

References 32 publications

(34 reference statements)

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“…Listeners use vowel variation for identifying speakers, dialects, and accents. Vowel inherent spectral change has been found to be central for differentiating regional variations of American English (Jacewicz and Fox, 2013), and vowel spectra also effectively differentiate American, British, and Australian English (Ghorshi et al, 2008). Listeners are most sensitive to indexical information in vowels rather than consonants: they are more effective at discriminating between speakers based on vowels than consonants (Owren & Cardillo, 2006), and are more likely to notice when vowels, rather than consonants, are replaced with tokens from other speakers (Hertz et al, 2004;Hertz, 2006).…”

Section: The Meaning Of Taumentioning

confidence: 99%

A unified account of categorical effects in phonetic perception

2016

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Categorical effects are found across speech sound categories, with the degree of these effects ranging from extremely strong categorical perception in consonants to nearly continuous perception in vowels. We show that both strong and weak categorical effects can be captured by a unified model. We treat speech perception as a statistical inference problem, assuming that listeners use their knowledge of categories as well as the acoustics of the signal to infer the intended productions of the speaker. Simulations show that the model provides close fits to empirical data, unifying past findings of categorical effects in consonants and vowels and capturing differences in the degree of categorical effects through a single parameter.

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Section: The Meaning Of Taumentioning

confidence: 99%

A unified account of categorical effects in phonetic perception

2016

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“…Previous research shows that acoustic parameters such as formant frequencies, pitch slope, phone duration and intensity of speech all contribute to accent to varying degrees (Zheng et al 2005;Yan et al 2003;Arslan and Hansen 1996;Scobbie et al 1999;Ghorshi et al 2008). Arslan and Hansen (1996) developed an algorithm based on isolated word and phoneme level to classify American English (AE) spoken in four variants: (i) received AE (i.e.…”

Section: Acoustic Characteristics Of Accentsmentioning

confidence: 99%

“…In their study, they noted that the phoneme duration pattern and the speaking rate also vary between accents and that the second formant F2 is most affected by accent. Ghorshi et al (2008) highlighted the differences in spectral features between British, Australian and American English accents through comparison of the cross-entropies of formants and cepstrum features. Their studies indicated that formants are better indicators of the three accents than cepstrum features.…”

Section: Acoustic Characteristics Of Accentsmentioning

confidence: 99%

“…A keyword lexicon such as that proposed by Fitt and Isard (1999) is one way to get round differences in accent for speech synthesis of different regional accents, where the sound is associated with a keyword rather than a phonetic symbol. Ghorshi et al (2008) present the formant spaces using the Arpabet symbols (AH, EY, etc.) for the vowels from Australian, British and American speakers.…”

Section: Vowel Extractionmentioning

confidence: 99%

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A new approach to acoustic analysis of two British regional accents—Birmingham and Liverpool accents

Zheng

Dyke

Berryman

et al. 2011

Int J Speech Technol

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Accent is a reflection of an individual speaker's regional affiliation and is shaped by the speaker's community background. This study investigated the acoustic characteristics of two British regional accents-the Birmingham and Liverpool accents-and their correlations from a different approach. In contrast to previous accent-related research, where the databases are formed from large groups of single-accent speakers, this study uses data from an individual who can speak in two accents, thus removing the effects of inter-speaker variability and facilitating efficient identification and analysis of the accent acoustic features. Acoustic features such as formant frequencies, pitch slope, intensity and phone duration have been used to investigate the prominent features of each accent. The acoustic analysis was based on nine monophthongal vowels and three diphthongal vowels. In addition, an analysis of variance of formant frequencies along the time dimension was performed to study the perceived effects of vocal tract shape changes as the speaker switches between the two accents. The results of the analysis indicate that the formant frequencies, pitch slope, the intensity and the phone duration all vary between the two accents. Classification testing using linear discriminant analysis showed that intensity had the strongest effect on differentiating between the two accents followed by F3, vowel duration, F2 and pitch slope.

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“…Auto-correlation and the average magnitude difference analysis will be disturbed by channel formant characteristics [3] [4], it can make the voice signal peak and valley values inconsistent with the pitch. Therefore, before signal analysis required prior to voice signal center clipping treatment.…”

Section: Pitch Extractionmentioning

confidence: 99%

Research on a new method of preprocessing and speech synthesis pitch detection

Yang

Liu

et al. 2010

2010 International Conference on Computer Design and Applications

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In order to solve pre-existing audio voice signal edge extraction is not accurate, easily lead to useful signal missing and the extraction of speech signals pitch detection easily misjudge the problems, proposed a threshold preprocessing voice method and an improved method of extracting the pitch period, we used moving window and polynomial smoothing method (Savitzky-Golay) smoothing the original speech signal, then preprocessing sound f"lIes by setting the amplitude threshold method to treat speech signal, and get ideal speech signals. Do processing by clipping voice signal center, Then use the modify autocorrelation function (MACF) and modify of the average magnitude difference function (W-AMDF) separately dealt with voice waveform, according to the characteristics of the waveform to processing signals , in order that troughs of the waveform position become the location of pitch period, thus the signal pitch period extracted.Experimental results show that the using the method can effectively improve the sound f"lIes of effective pre-signal extraction accuracy, improve the efficiency of signal processing and reduced the pitch detection misjudge significantly.

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Cross-entropic comparison of formants of British, Australian and American English accents

Cited by 15 publications

References 32 publications

A unified account of categorical effects in phonetic perception

A unified account of categorical effects in phonetic perception

A new approach to acoustic analysis of two British regional accents—Birmingham and Liverpool accents

Research on a new method of preprocessing and speech synthesis pitch detection

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