Kurdish Dialects and Neighbor Languages Automatic Recognition

Dialect recognition is one of the most hot topics in the speech analysis area. In this study a system for dialect and language recognition is developed using phonetic and a style based features. The study suggests a new set of feature using one-dimensional LBP feature.  The results show that the proposed LBP set of feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini and Hawrami) with three neighbor languages (Arabic, Persian and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages

Kurdish Dialect Recognition using 1D CNN

Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects

The production and perception of peripheral geminate/singleton coronal stop contrasts in Arabic

Gemination is typologically common word-medially but is rare at the periphery of the word (word-initially and -finally). In line with this observation, prior research on production and perception of gemination has focused primarily on medial gemination. Much less is known about the production and perception of peripheral gemination. This PhD thesis reports on comprehensive articulatory, acoustic and perceptual investigations of geminate-singleton contrasts according to the position of the contrast in the word and in the utterance. The production component of the project investigated the articulatory and acoustic features of medial and peripheral gemination of voiced and voiceless coronal stops in Modern standard Arabic and regional Arabic vernacular dialects, as produced by speakers from two disparate and geographically distant countries, Morocco and Lebanon. The perceptual experiment investigated how standard and dialectal Arabic gemination contrasts in each word position were categorised and discriminated by three groups of non-native listeners, each differing in their native language experience with gemination at different word positions. The first experiment used ultrasound and acoustic recordings to address the extent to which word-initial gemination in Moroccan and Lebanese dialectal Arabic is maintained, as well as the articulatory and acoustic variability of the contrast according to the position of the gemination contrast in the utterance (initial vs. medial) and between the two dialects. The second experiment compared the production of word-medial and -final gemination in Modern Standard Arabic as produced by Moroccan and Lebanese speakers. The aim of the perceptual experiment was to disentangle the contribution of phonological and phonetic effects of the listeners’ native languages on the categorisation and discrimination of non-lexical Moroccan gemination by three groups of non-native listeners varying in their phonological (native Lebanese group and heritage Lebanese group, for whom Moroccan is unintelligible, i.e., non-native language) and phonetic-only (native English group) experience with gemination across the three word positions. The findings in this thesis constitute important contributions about positional and dialectal effects on the production and perception of gemination contrasts, going beyond medial gemination (which was mainly included as control) and illuminating in particular the typologically rare peripheral gemination

Characterizing phonetic transformations and fine-grained acoustic differences across dialects

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 169-175).This thesis is motivated by the gaps between speech science and technology in analyzing dialects. In speech science, investigating phonetic rules is usually manually laborious and time consuming, limiting the amount of data analyzed. Without sufficient data, the analysis could potentially overlook or over-specify certain phonetic rules. On the other hand, in speech technology such as automatic dialect recognition, phonetic rules are rarely modeled explicitly. While many applications do not require such knowledge to obtain good performance, it is beneficial to specifically model pronunciation patterns in certain applications. For example, users of language learning software can benefit from explicit and intuitive feedback from the computer to alter their pronunciation; in forensic phonetics, it is important that results of automated systems are justifiable on phonetic grounds. In this work, we propose a mathematical framework to analyze dialects in terms of (1) phonetic transformations and (2) acoustic differences. The proposed Phonetic based Pronunciation Model (PPM) uses a hidden Markov model to characterize when and how often substitutions, insertions, and deletions occur. In particular, clustering methods are compared to better model deletion transformations. In addition, an acoustic counterpart of PPM, Acoustic-based Pronunciation Model (APM), is proposed to characterize and locate fine-grained acoustic differences such as formant transitions and nasalization across dialects. We used three data sets to empirically compare the proposed models in Arabic and English dialects. Results in automatic dialect recognition demonstrate that the proposed models complement standard baseline systems. Results in pronunciation generation and rule retrieval experiments indicate that the proposed models learn underlying phonetic rules across dialects. Our proposed system postulates pronunciation rules to a phonetician who interprets and refines them to discover new rules or quantify known rules. This can be done on large corpora to develop rules of greater statistical significance than has previously been possible. Potential applications of this work include speaker characterization and recognition, automatic dialect recognition, automatic speech recognition and synthesis, forensic phonetics, language learning or accent training education, and assistive diagnosis tools for speech and voice disorders.by Nancy Fang-Yih Chen.Ph.D

An Affiliative Model of Early Lexical Learning

In defining the language acquisition problem, traditional models abstract away effects of variability, defining the learner as acquiring a single language variety, which is spoken homogeneously by their speech community. However, infants are exposed to as many unique varieties of speech as they are speakers. Adult sociolinguistic competence is also characterized by the capacity to employ and interpret non-phonological linguistic distinctions which are associated with different social groups, including ‘code-switching’ or ‘style-shifting’ between languages and speech registers. This dissertation presents a model of infant lexical acquisition which assumes that learners monitor linguistic sources for variation in reliability. This model is adapted from Shafto, Eaves, Navarro, and Perfors (2012) which the authors used to describe the behavior of preschool children in selecting sources to learn labels from in K. Corriveau and Harris (2009) and M. Corriveau and Harris (2009). I show that this probabilistic model effectively simulates two experiments from the literature on preverbal infants’ perception of labeling, Rost and McMurray (2009) and Koenig and Echols (2003). Evidence suggests that the receptiveness of preverbal infants to novel lexical items is correlated with infant beliefs regarding the informant’s knowledgeability and social group membership. These simulations demonstrate that language learners may well be recruiting processes of epistemic trust to guide lexical acquisition much earlier than previously suggested. We should therefore expect even very young listeners to respond differently to dialects not solely as a function of exposure, but also as a function of attitudes towards the speech determined by the quality of that exposure. Developmental differences between populations in attention to non-linguistic affiliative cues are therefore expected to emerge early and have significant effects on language outcomes. Measures of online language proficiency may be vulnerable to significant bias owing to the activation of sociolinguistic biases in the presentation of test items. Differences in the breadth or specificity of listener preferences for speakers in turn predict differences in task complexity for learners of standard and non-standard dialects. A new research program in early sociophonetic perception, uniting accounts of selective trust with language learning has the potential to deepen understanding of both typical and disordered language development

Automatic Dialect and Accent Recognition and its Application to Speech Recognition

A fundamental challenge for current research on speech science and technology is understanding and modeling individual variation in spoken language. Individuals have their own speaking styles, depending on many factors, such as their dialect and accent as well as their socioeconomic background. These individual differences typically introduce modeling difficulties for large-scale speaker-independent systems designed to process input from any variant of a given language. This dissertation focuses on automatically identifying the dialect or accent of a speaker given a sample of their speech, and demonstrates how such a technology can be employed to improve Automatic Speech Recognition (ASR). In this thesis, we describe a variety of approaches that make use of multiple streams of information in the acoustic signal to build a system that recognizes the regional dialect and accent of a speaker. In particular, we examine frame-based acoustic, phonetic, and phonotactic features, as well as high-level prosodic features, comparing generative and discriminative modeling techniques. We first analyze the effectiveness of approaches to language identification that have been successfully employed by that community, applying them here to dialect identification. We next show how we can improve upon these techniques. Finally, we introduce several novel modeling approaches -- Discriminative Phonotactics and kernel-based methods. We test our best performing approach on four broad Arabic dialects, ten Arabic sub-dialects, American English vs. Indian English accents, American English Southern vs. Non-Southern, American dialects at the state level plus Canada, and three Portuguese dialects. Our experiments demonstrate that our novel approach, which relies on the hypothesis that certain phones are realized differently across dialects, achieves new state-of-the-art performance on most dialect recognition tasks. This approach achieves an Equal Error Rate (EER) of 4% for four broad Arabic dialects, an EER of 6.3% for American vs. Indian English accents, 14.6% for American English Southern vs. Non-Southern dialects, and 7.9% for three Portuguese dialects. Our framework can also be used to automatically extract linguistic knowledge, specifically the context-dependent phonetic cues that may distinguish one dialect form another. We illustrate the efficacy of our approach by demonstrating the correlation of our results with geographical proximity of the various dialects. As a final measure of the utility of our studies, we also show that, it is possible to improve ASR. Employing our dialect identification system prior to ASR to identify the Levantine Arabic dialect in mixed speech of a variety of dialects allows us to optimize the engine's language model and use Levantine-specific acoustic models where appropriate. This procedure improves the Word Error Rate (WER) for Levantine by 4.6% absolute; 9.3% relative. In addition, we demonstrate in this thesis that, using a linguistically-motivated pronunciation modeling approach, we can improve the WER of a state-of-the art ASR system by 2.2% absolute and 11.5% relative WER on Modern Standard Arabic

A description of the rhythm of Barunga Kriol using rhythm metrics and an analysis of vowel reduction

Kriol is an English-lexifier creole language spoken by over 20,000 children and adults in the Northern parts of Australia, yet much about the prosody of this language remains unknown. This thesis provides a preliminary description of the rhythm and patterns of vowel reduction of Barunga Kriol - a variety of Kriol local to Barunga Community, NT – and compares it to a relatively standard variety of Australian English. The thesis is divided into two studies. Study 1, the Rhythm Metric Study, describes the rhythm of Barunga Kriol and Australian English using rhythm metrics. Study 2, the Vowel Reduction Study, compared patterns of vowel reduction in Barunga Kriol and Australian English. This thesis contributes the first in depth studies of vowel reduction patterns and rhythm using rhythm metrics in any variety of Kriol or Australian English. The research also sets an adult baseline for metric results and patterns of vowel reduction for Barunga Kriol and Australian English, useful for future studies of child speech in these varieties. As rhythm is a major contributor to intelligibility, the findings of this thesis have the potential to inform teaching practice in English as a Second Language

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

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