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However, the deeper the study of features, the harder but unfortunately the GMM was not capable of providing it was to solve the problem and the more time was needed to information on the temporal relationships due to lack of get results. Studying the acoustics and phonetics the current phone. As a result, researchers looked at HMM to features of speech cannot be ignored, but at the same time they resolve this problem because HMM involves memory. In other words, this kind of 2 HMM study was unable to provide a generalized solution.

So, the The HMM as does the GMM assume dialect to be direction now is to look at approaches that imitate human random processes, that can move from one state to another perception in classifying and identifying dialects. Each state is associated with a process, which generates a new state with a new probability. From a methodology point of view is called Hidden as the states are hidden and recognition Most if not all of the methods that were used for language allows finding those states.

In case first and simplest method. GMM models utterances and of dialects, there will be a separate model for each dialect. By computing the likelihood of these processes, the dialect specific phone modeling [17].

The test utterance is then process with the highest likelihood determines the identified compared to each model, resulting in a score. The model dialect [35]. A few studies have used GMM for dialect identification. The same phone models can be applied for dialect One of these was that described by Chen, Chang, and Wang to identification.

The identified accent type can then be the second type representing sounds unique to a specific used to select an accent-dependent model for speech dialect. The common states with same sounds were connected recognition. Different speech features such as time Another attempt was that of Faria in which a GMM derivatives, energy, and the shifted delta cepstra were utilized classifier was used to recognize native and non-native speech to model the phonetic differences between Egyptian and Gulf [37]. This attempt aimed to distinguish between Native Arabic.

A detailed comparison of the designed Arabic dialect American English and non-native English using speech from identification system using the different speech feature Russian, Spanish, French, German, Chinese, Indian, and other combinations was presented.

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The designed system achieved speakers. This experiment verified that the lexical features accuracy of However, when the speech sound falls within words and four test sentences. The selected database was built common states, the system has to find the difference by using head mounted microphone and telephone recordings. Fourty-eight male speakers from Duke University participated in the experiment to represent neutral American, German, In [40], A.

Arslan used 5 tokens of configurations to identify five South African English dialects: 12 speakers for each accent group 60 tokens. Their algorithm outperformed the average human African English.

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Seven tests were used to evaluate the listener. The best model was the first one, which applied configurations, with different speech duration for each string. HMM on isolated words; its accuracy was Coming The findings were that the system which used the second order next the HMM that processed the mono-phone models within ergodic HMMs was the best performing and the identification the actual utterance, which achieved accuracy equal to Arslan and Hensen found that as the test utterance the obtained results showed a linear relationship between length increased, higher classification accuracy was achieved accuracy and length of test segment, and that the longer the coincident with A.

An accent speech duration the higher the accuracy achieved. The new different language accents [42]. In addition, it was found that approach was based on phoneme segmentation. A Parallel some words are better accent discriminators than others. Phoneme Recognition PPR system was developed.

The Towards the same objective of employing more classifier was designed to process continuous speech and to distinguish between native Australian English AuE speakers discriminative classifiers, Ma, Zhu, and Tong used multi- and two migrant speaker groups with foreign accents, whose dimensional pitch flux and MFCC features to discriminate first languages are Lebanese Arabic LA and South between three Chinese dialects [43].

The multidimensional Vietnamese SV. The utterances for Australian Au English speakers, for duration of the test utterances was in the range of 3 to 15s, and Lebanese Arabic LA speakers, and utterances for the system was able to recognize the three dialects with an South Vietnamese SV English speakers.

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Zhu and Tong concluded that increasing the novel, requiring no manually labeled accent data, and can be duration of a test segment has a positive impact on both automated. The test utterances were processed in parallel by accuracy and error rate, as increasing the test segment duration the three accent recognizers. The recognizers employed accent leads to an increase in accuracy this again coincides with specific HMMs and phone bigram language models to conclusions by A.

The results of decrease in error rate. In distinguish between speaker accents.

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The identifier was able both experiments, listeners were subjected to regional to differentiate between the three accents; the identification intonational contours of German. In this study, listener accuracy achieved was Also, English, and The average phoneme listeners with non-local variety in their personal experience accent classification was King explained that the accent specific phoneme language variety only. At the same time, listeners who were not familiar models did not provide the expected significant contribution with Hamburg German and Berlin German were more due to the limited training data.

In addition, the classifier was successful in the identification test. The conclusion drawn was very sensitive to the available data and to speaker variations. In addition, the choice of the techniques. Moreover, the author suggested creating speaker generating the carrier utterances played an important benchmarks for speaker accent classification based on human factor that may affect recognizer performance.

The perception studies for future work. Following along the same lines, other studies using new Searching for more reliable cues for Automatic Dialect sensitive word lists were executed by Arslan and Hensen [42]. Identification ADI , a study was done to explore the utility of Three models were employed for accent classification, the prosodic features in language identification tasks and to check first was to apply HMM on isolated words for individual their reliability in the discrimination of Arabic dialects [45].

V. The Indexing of Dialect Data

Furthermore, Arabic listeners were mono-phone models but considering the ones that actually successful in identifying the Arabic dialects in their natural exist in the utterance. After extensive study of the American and synthesized forms. Teixeira, Trancoso, and Serralheiro believed achieved higher rate of identification for their own dialect. In [49], a PRLM configuration for dialect identification was developed. The work aimed to identify Spanish dialect Based on the same concept of reliable cues, Hamdi and from Cuban and Peruvian dialects.

The Phonotactic languages with different rhythm categories [46]. Preliminary LMs trained on Cuban and Peruvian speech. The duration of results revealed that listeners use rhythm cues in the each utterance was 3 minutes each, recorded from 40 Cuban identification process, and listeners were able to distinguish and 20 Peruvian speakers. Experiments prove that all Arabic dialects In [50], a PPRLM and a logistic back-end classifier to are still clustered within the stress-timed languages and exhibit identify five Arabic dialects were employed.

The five Arabic a different distribution from languages belonging to other dialects were Egyptian, Gulf, Iraqi, Levantine, and Modern rhythmic categories, such as French and Catalan. By using this Standard Arabic. Different phone recognizers such as: Modern method, the complexity of the syllabic structure of the dialect Standard Arabic, English, German, Japanese, Hindi, as in the word cat in the English language which will be Mandarin, and Spanish were used.

Using 30 second test reduction acoustic features that may lead to shorten vowel utterances the recognizer was able to identify the dialects with pronunciation were extracted. Since rhythmic analysis is one accuracy reaching The training sets used were This The test sets method achieved limited success due to lack of sufficient data consisted of 6. The best improvements. South African English was classified and represents an important key factor in dialect identification. The black South African group included two accents: approach to adapt bi-phones for dialect identification.

Both Nguni and Sotho. The PPR method was applied for the supervised and unsupervised learning algorithms were automatic classification process. The main objective of this applied. Using these algorithms, dialect discriminating work was to determine whether black South African English phonetic rules were extracted.

Also, dialect discriminating bi- has to be treated separately when used in ASR or should be phones which are compatible with the linguistic literature treated as belonging to the single variety of South African were discovered, and at the same time improved a baseline English. The results were as follows: the system was able to mono-phone system by 7. The proposed dialect classify the two groups, white and black South African discriminating bi-phone system produced relative gains of English; however it was unable to distinguish between Nguni The work of Chen and and Sotho speakers.

Finally, the decision was made to Campbell was considered a first step towards a linguistically- consider Nguni and Sotho native speakers English as a single informative dialect recognition system, and - as they stated - if variety during the ASR development phase. The training set word transcriptions were provided, the proposed system can was 2. It can discriminate between two groups words repeated two times for each accent. The test utterance was recognized The main advantage of SVM is that if there is no using accent-specific phone HMMs; an accuracy of The transformed, by using a kernel function, to another space experiments were done on a comparison basis, accent-specific where they can then be discriminated.

The original SVM speech recognition was used and the accuracy achieved was works for two way classification, however there is a multiclass Two SVMs were task was used. The focus was on two parts of the system: the used. The word intonation duration, F2 and F3 contour, acoustic feature extraction a comparison was done on FFT- and the word final stop closure duration were the measured based analysis with IIR and FIR filter banks in terms of their parameters for classification.

The conclusion was that Another effective approach for DI is the cluster technique, the GPU implementation is a better solution when processing which is a good choice when we are looking for a time is a concern. A method of clustering dialects Looking for new effective models that can replace humans using their common phonological features was developed in the recognition process there was an exploration of several [32]. The work aimed at examining the statistical co-variation Neural Network NN models that can be used in speech of dialects and their dependency. The method was based on systems [54].

These variables seem to be were promising, and the developed prosodic models were independent of each other. However, they do exhibit statistical suggested to be explored with the conventional models for dependence. The method was tested by exploring a data set of improving speech, speaker, and language recognition system binary features describing the pronunciation of vowels performance.

Phonological Variants and Dialect Identification In Latin American Spanish

In addition, it was found that the developed and consonants of English speakers from 35 countries and prosodic models can enhance the quality of the synthesized regions. These dialects were grouped according to patterns of speech. The MI is defined x y models to distinguish five Hindi dialects using spectral and prosodic features. This was the reason V. This idea was Arslan showed that working with isolated words is better successful in reflecting the historical relationships between than working with phone models when dealing with local dialects.

A proof by experiment that accents with historical dialects [42]. Zhang worked on words derived from lexical relationships may share common acoustics and phonological sets to find the Mutual Information between different dialects features was provided they are correlated.

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The results of this [32]. Combining phonotactic constraints with HMM improves method of categorizing dialect varieties by binary ADI as the phonotactic constraints reflect more information pronunciation features were compared to traditional groupings than that of a word. The SVM is a promising technique for based on external features. This work will help provide the number of accents.

More research is needed to highlight the universal pattern variation for the English dialects. Several works stressed the linear relationship One of the recent works that focus on the importance of between the length of test segments and accuracy, the longer speedy recognizers with new technologies was that of Hanani the test segment the higher the accuracy obtained.

The work explored the application of Graphics Through a broad view of the dialect identification problem Processing Units GPUs to speech pattern processing to we have seen that both temporal and prosodic features are provide substantial processing power at low cost. In addition, very important as they can reflect more detailed information it demonstrated the principles for using GPUs such as about the different patterns a word may have across one algorithm selection and effective coding.


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All Rights Reserved. Close Modal. Among the proposed factors that influence linguistic behavior in border communities are physical and political ease of border crossing, inter-nation economic imbalances, proximity of major urban areas, trans-border indigenous communities, relative proportion of locally-born residents, and historical rivalries and conflicts. In each of the scenarios, variations in the relative importance of these factors yields a different sociolinguistic configuration. In the court of public opinion and in many monographic studies, Latin American Spanish dialects are defined by national boundaries, thus Mexican Spanish, Argentine Spanish, Peruvian Spanish, etc.

Objectively, such a scheme cannot be seriously maintained; Latin American Spanish is roughly divided into geographical dialect zones based on patterns of settlement and colonial administration, contact with indigenous and immigrant languages, and relative proportions of rural and urban speech communities Lipski , The prevailing nation-centered approach to Spanish American dialectology is augmented by the focus on the speech of the nations' capital cities, which often exert a demographically disproportionate linguistic influence on the remainder of the country Lipski As a result, the research bibliography on far-flung regional varieties is woefully incomplete, and given the fact that few capitals or major urban centers are situated along national borders, there is relatively little information on microdialectal variation in border regions.

This contrasts sharply with studies focusing on border areas involving separate languages, for example Spanish-Portuguese along the border with Brazil or along the Spain-Portugal border, 1 Spanish-Kreyol along the Dominican-Haitian border Diaz , Ortiz , or Spanish-English near the United States-Mexican border Hidalgo , , , For reasons of brevity, the following overview will focus on contact among Spanish dialects along international borders separating Spanish-speaking nations.