A study of temporal features and frequency characteristics in American English foreign accent

Abstract

In this paper, a detailed acoustic study of foreign accent is proposed using temporal features, intonation patterns, and frequency characteristics in American English. Using a database which consists of words uttered in isolation, temporal features such as voice onset time, word-final stop closure duration, and characteristics of duration are investigated. Accent differences for native-produced versus Mandarin, German, and Turkish accented English utterances are analyzed. Of the dimensions considered, the most important accent relayer is found to be word-final stop closure duration. Mandarin accented English utterances show significant differences in terms of this feature when compared to native speaker utterances. In addition, the intonation characteristics across a set of foreign accents in American English is investigated. It is shown that Mandarin speaker utterances possess a larger negative continuative intonation slope than native speaker utterances, and German speaker utterances had a more positive intonation slope when compared to native speaker utterances. Finally, a detailed frequency analysis of foreign accented speech is conducted. It is shown that the midfrequency range (1500–2500 Hz) is the most sensitive frequency band to non-native speaker pronunciation variations. Based on this knowledge a new frequency scale for the calculation of cepstrum coefficients is formulated which is shown to outperform the Mel-scale in terms of its ability to classify accent automatically among four accent classes.

Chinese Accents Identification with Modified MFCC

“…One of the key steps to get the parameters is dividing the linear frequency into triangular filter bank and output the bandwidth energy from each filter. The influence degrees of accent to formants are different [7]. Therefore, an algorithm to improve accent identification rate is to change the triangle filter density (such as increasing the triangular filter numbers of more sensitive frequency) of MFCC.…”

“…[8] applies it to improve speaker identification rate in whispered speech since the locations of the formants and the auditory model in whispered speech are different from those in normal speech. [7] discovers that middle frequencies ( 1500~2500Hz) are sensitive to American English foreign accents and then proposes a modifying MFCC algorithm that increases middle filter density of MFCC. The speech recognition rate based on modified MFCC is higher than system based on traditional MFCC by 1.2%.…”

“…The speech recognition rate based on modified MFCC is higher than system based on traditional MFCC by 1.2%. Experimental corpus of [7] is only 20 independent words and four sentences. Overall, the studies of modifying MFCC are not enough.…”

SpeechFind: advances in spoken document retrieval for a National Gallery of the Spoken Word

“…In the field of robust speech recognition, there is a variety challenging problems that persist, such as reliable speech recognition across wireless communications channels, recognition of speech across changing speaker conditions (emotion and stress [25]- [27], accent [28], [29]), or recognition of speech from unknown or changing acoustic environments. The ability to achieve effective performance in changing speaker conditions for large vocabulary continuous speech recognition (LVCSR) remains a challenge, as demonstrated in recent DARPA evaluations focused on Broadcast News (BN) versus previous results from the Wall Street Journal (WSJ) corpus.…”

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

“…Speech formants carry information regarding phonemic labels [31], speaker identity and accent characteristics [32]. Although formant analysis has received considerable attention and a variety of automated approaches [33,34] have been developed, the estimation of accurate formant features from the speech signal is a non-trivial problem that attracts continued research.…”