Pronunciation Distance Research Articles

"They sure aren't from around here": Children's perception of accent distance in L1 and L2 varieties of English.

Children exhibit preferences for familiar accents early in life. However, they frequently have more difficulty distinguishing between first language (L1) accents than second language (L2) accents in categorization tasks. Few studies have addressed children's perception of accent strength, or the relation between accent strength and objective measures of pronunciation distance. To address these gaps, 6- and 12-year-olds and adults ranked talkers' perceived distance from the local accent (i.e., Midland American English). Rankings were compared with objective distance measures. Acoustic and phonetic distance measures were significant predictors of ladder rankings, but there was no evidence that children and adults significantly differed in their sensitivity to accent strength. Levenshtein Distance, a phonetic distance metric, was the strongest predictor of perceptual rankings for both children and adults. As a percept, accent strength has critical implications for social judgments, which determine real world social outcomes for talkers with non-local accents.

Design of the Violin Performance Evaluation System Based on Mobile Terminal Technology

In order to solve the problem that violin performance evaluation is too subjective, this paper proposes a violin performance evaluation system based on mobile terminal technology. Violin sound evaluation mainly includes two indicators such as pronunciation quality and distance transmission ability. LabVIEW 8.2 is used to collect data, and fast Fourier transform (FFT) is used to analyze the time-domain signal in frequency domain, and the pronunciation process of violin is discussed in depth. Using statistical methods to process the test data of pronunciation distance transmission, a parameter model representing the pronunciation distance transmission ability is proposed, and finally the WeChat applet is used to control the recording and display. The results show that the process of violin sound production is accompanied by the weakening of octave peaks and the development of overtone peaks. The stable stage is marked by the stability of each octave peak and overtone peak. The sound head initially presents a simple excitation response, with only one near octave overtone and three cycles. Its maximum overtone peak/dominant frequency peak ratio coefficient is k = 0.36; the back entry complex excitation response has about nine cycles, with k = 0.735. Then enter the quasi-stable excitation response, whose waveform is similar to the typical A-string empty string pull waveform in the later stage, with k = 0.36; the maximum overtone peak/main frequency peak proportional coefficient is k = 0.39 in the rising section of the start, k = 0.2 in the loudest section, and k = 0.26 in the stable section. Conclusion. The system has initially established a new instrumented evaluation method, which provides a way of thinking for improving the quantifiable evaluation system in the future.

How pronunciation distance impacts word recognition in children and adults.

Although unfamiliar accents can pose word identification challenges for children and adults, few studies have directly compared perception of multiple nonnative and regional accents or quantified how the extent of deviation from the ambient accent impacts word identification accuracy across development. To address these gaps, 5- to 7-year-old children's and adults' word identification accuracy with native (Midland American, British, Scottish), nonnative (German-, Mandarin-, Japanese-accented English) and bilingual (Hindi-English) varieties (one talker per accent) was tested in quiet and noise. Talkers' pronunciation distance from the ambient dialect was quantified at the phoneme level using a Levenshtein algorithm adaptation. Whereas performance was worse on all non-ambient dialects than the ambient one, there were only interactions between talker and age (child vs adult or across age for the children) for a subset of talkers, which did not fall along the native/nonnative divide. Levenshtein distances significantly predicted word recognition accuracy for adults and children in both listening environments with similar impacts in quiet. In noise, children had more difficulty overcoming pronunciations that substantially deviated from ambient dialect norms than adults. Future work should continue investigating how pronunciation distance impacts word recognition accuracy by incorporating distance metrics at other levels of analysis (e.g., phonetic, suprasegmental).

A New Acoustic-Based Pronunciation Distance Measure.

We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing speech of non-native American-English speakers to a collection of native American-English speakers. An acoustic-only measure is valuable as it does not require the time-consuming and error-prone process of phonetically transcribing speech samples which is necessary for current edit distance-based approaches. We minimize speaker variability in the data set by employing speaker-based cepstral mean and variance normalization, and compute word-based acoustic distances using the dynamic time warping algorithm. Our results indicate a strong correlation of r = −0.71 (p < 0.0001) between the acoustic distances and human judgments of native-likeness provided by more than 1,100 native American-English raters. Therefore, the convenient acoustic measure performs only slightly lower than the state-of-the-art transcription-based performance of r = −0.77. We also report the results of several small experiments which show that the acoustic measure is not only sensitive to segmental differences, but also to intonational differences and durational differences. However, it is not immune to unwanted differences caused by using a different recording device.

AFRIKAANS AND DUTCH AS CLOSELY-RELATED LANGUAGES: A COMPARISON TO WEST GERMANIC LANGUAGES AND DUTCH DIALECTS

Following Den Besten’s (2009) desiderata for historical linguistics of Afrikaans, this article aims to contribute some modern evidence to the debate regarding the founding dialects of Afrikaans. From an applied perspective (i.e. human language technology), we aim to determine which West Germanic language(s) and/or dialect(s) would be best suited for the purposes of recycling speech resources for the benefit of developing speech technologies for Afrikaans. Being recognised as a West Germanic language, Afrikaans is first compared to Standard Dutch, Standard Frisian and Standard German. Pronunciation distances are measured by means of Levenshtein distances. Afrikaans is found to be closest to Standard Dutch. Secondly, Afrikaans is compared to 361 Dutch dialectal varieties in the Netherlands and North-Belgium, using material from the Reeks Nederlandse Dialectatlassen , a series of dialect atlases compiled by Blancquaert and Pee in the period 1925-1982 which cover the Dutch dialect area. Afrikaans is found to be closest to the South-Holland dialectal variety of Zoetermeer; this largely agrees with the findings of Kloeke (1950). No speech resources are available for Zoetermeer, but such resources are available for Standard Dutch. Although the dialect of Zoetermeer is significantly closer to Afrikaans than Standard Dutch is, Standard Dutch speech resources might be a good substitute.

A Cognitively Grounded Measure of Pronunciation Distance

In this study we develop pronunciation distances based on naive discriminative learning (NDL). Measures of pronunciation distance are used in several subfields of linguistics, including psycholinguistics, dialectology and typology. In contrast to the commonly used Levenshtein algorithm, NDL is grounded in cognitive theory of competitive reinforcement learning and is able to generate asymmetrical pronunciation distances. In a first study, we validated the NDL-based pronunciation distances by comparing them to a large set of native-likeness ratings given by native American English speakers when presented with accented English speech. In a second study, the NDL-based pronunciation distances were validated on the basis of perceptual dialect distances of Norwegian speakers. Results indicated that the NDL-based pronunciation distances matched perceptual distances reasonably well with correlations ranging between 0.7 and 0.8. While the correlations were comparable to those obtained using the Levenshtein distance, the NDL-based approach is more flexible as it is also able to incorporate acoustic information other than sound segments.

Diatopic Patterning of Croatian Varieties in the Adriatic Region

The calculation of aggregate linguistic distances can compensate for some of the drawbacks inherent to the isogloss bundling method used in traditional dialectology to identify dialect areas. Synchronic aggregate analysis can also point out differences with respect to a diachronically based classification of dialects. In this study the Levenshtein algorithm is applied for the first time to obtain an aggregate analysis of the linguistic distances among 88 diatopic varieties of Croatian spoken along the Eastern Adriatic coast and in the Italian province of Molise. We also measured lexical differences among these varieties, which are traditionally grouped into Čakavian, Štokavian, and transitional Čakavian-Štokavian varieties. The lexical and pronunciation distances are subsequently projected onto multidimensional cartographic representations. Both kinds of analyses confirm that linguistic discontinuity is characteristic of the whole region, and that discontinuities are more pronounced in the northern Adriatic area than in the south. We also show that the geographic lines are in many cases the most decisive factor contributing to linguistic cohesion, and that the internal heterogeneity within Čakavian is often greater than the differences between Čakavian and Štokavian varieties. This holds both for pronunciation and lexicon.

Quantitative Social Dialectology: Explaining Linguistic Variation Geographically and Socially

In this study we examine linguistic variation and its dependence on both social and geographic factors. We follow dialectometry in applying a quantitative methodology and focusing on dialect distances, and social dialectology in the choice of factors we examine in building a model to predict word pronunciation distances from the standard Dutch language to 424 Dutch dialects. We combine linear mixed-effects regression modeling with generalized additive modeling to predict the pronunciation distance of 559 words. Although geographical position is the dominant predictor, several other factors emerged as significant. The model predicts a greater distance from the standard for smaller communities, for communities with a higher average age, for nouns (as contrasted with verbs and adjectives), for more frequent words, and for words with relatively many vowels. The impact of the demographic variables, however, varied from word to word. For a majority of words, larger, richer and younger communities are moving towards the standard. For a smaller minority of words, larger, richer and younger communities emerge as driving a change away from the standard. Similarly, the strength of the effects of word frequency and word category varied geographically. The peripheral areas of the Netherlands showed a greater distance from the standard for nouns (as opposed to verbs and adjectives) as well as for high-frequency words, compared to the more central areas. Our findings indicate that changes in pronunciation have been spreading (in particular for low-frequency words) from the Hollandic center of economic power to the peripheral areas of the country, meeting resistance that is stronger wherever, for well-documented historical reasons, the political influence of Holland was reduced. Our results are also consistent with the theory of lexical diffusion, in that distances from the Hollandic norm vary systematically and predictably on a word by word basis.

Speech driven realistic mouth animation based on multi-modal unit selection

This paper presents a novel audio visual diviseme (viseme pair) instance selection and concatenation method for speech driven photo realistic mouth animation. Firstly, an audio visual diviseme database is built consisting of the audio feature sequences, intensity sequences and visual feature sequences of the instances. In the Viterbi based diviseme instance selection, we set the accumulative cost as the weighted sum of three items: 1) logarithm of concatenation smoothness of the synthesized mouth trajectory; 2) logarithm of the pronunciation distance; 3) logarithm of the audio intensity distance between the candidate diviseme instance and the target diviseme segment in the incoming speech. The selected diviseme instances are time warped and blended to construct the mouth animation. Objective and subjective evaluations on the synthesized mouth animations prove that the multimodal diviseme instance selection algorithm proposed in this paper outperforms the triphone unit selection algorithm in Video Rewrite. Clear, accurate, smooth mouth animations can be obtained matching well with the pronunciation and intensity changes in the incoming speech. Moreover, with the logarithm function in the accumulative cost, it is easy to set the weights to obtain optimal mouth animations.

Systems and methods for combining subword recognition and whole word recognition of a spoken input

A computer-based detection (e.g., speech recognition) system combines a word decoder and subword decoder to detect words (or phrases) in a spoken input provided by a user into a speaker connected to the detection system. The word decoder detects words by comparing an input pattern (e.g., of hypothetical word matches) to reference patterns (e.g., words). The subword decoder compares an input pattern (e.g., hypothetical words matches based on subword or phoneme recognition) to reference patterns (e.g., words) based on a word pronunciation distance measure that indicates how close each input pattern is to matching each reference pattern. The subword decoder sorts the source set of reference patterns based on a closeness of each reference pattern to correctly matching the input pattern based on generated pattern comparisons. The word decoder and subword decoder each provide an N-best list of hypothetical matches to the spoken input. A list fusion module of the detection system selectively combines the two N-best lists to produce a final or combined N-best list. The final or combined list has a predefined number of matches.

Dialect areas and dialect continua

The organizing concept behind dialect variation is still seen predominantly as the areas within which similar varieties are spoken. The opposing view—that dialects are organized in a continuum without sharp boundaries—is likewise popular. This article introduces a new element into the discussion, which is the opportunity to view dialectal differences in the aggregate. We employ a dialectometric technique that provides an additive measure of pronunciation difference: the (aggregate) pronunciation distance. This allows us to determine how much of the linguistic variation is accounted for by geography. In our sample of 27 Dutch towns and villages, the variation ranges between 65% and 81%, which lends credence to the continuum view. The borders of well-established dialect areas nonetheless show large deviations from the expected aggregate pronunciation distance. We pay particular attention to a puzzle concerning the subjective perception of continua introduced by Chambers and Trudgill (1998): a traveller walking in a straight line from village to village notices successive small changes, but seldom, if ever, observes large differences. This sounds like a justification of the continuum view, but there is an added twist. Might the traveller be misled by the perspective of most recent memory? We use the Chambers–Trudgill puzzle to organize our argument at several points.