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

Culture Clubs: Processing Speech by Deriving and Exploiting Linguistic Subcultures

Spoken language understanding systems are error-prone for several reasons, including individual speech variability. This is manifested in many ways, among which are differences in pronunciation, lexical inventory, grammar and disfluencies. There is, however, a lot of evidence pointing to stable language usage within subgroups of a language population. We call these subgroups linguistic subcultures. The two broad problems are defined and a survey of the work in this space is performed. The two broad problems are: linguistic subculture detection, commonly performed via Language Identification, Accent Identification or Dialect Identification approaches; and speech and language processing tasks taken which may see increases in performance by modeling for each linguistic subculture. The data used in the experiments are drawn from four corpora: Accents of the British Isles (ABI), Intonational Variation in English (IViE), the NIST Language Recognition Evaluation Plan (LRE15) and Switchboard. The speakers in the corpora come from different parts of the United Kingdom and the United States and were provided different stimuli. From the speech samples, two features sets are used in the experiments. A number of experiments to determine linguistic subcultures are conducted. The set of experiments cover a number of approaches including the use traditional machine learning approaches shown to be effective for similar tasks in the past, each with multiple feature sets. State-of-the-art deep learning approaches are also applied to this problem. Two large automatic speech recognition (ASR) experiments are performed against all three corpora: one, monolithic experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. For the discourse markers labeled in the Switchboard corpus, there are some interesting trends when examined through the lens of the speakers in their linguistic subcultures. Two large dialogue acts experiments are performed against the labeled portion of the Switchboard corpus: one, monocultural (or monolithic ) experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. We conclude by discussing applications of this work, the changing landscape of natural language processing and suggestions for future research

Automatic Identification of Arabic Dialects USING Hidden Markov Models

The Arabic language has many different dialects, they must beidentified before Automatic Speech Recognition can take place.This thesis examines the difficult task of properly identifyingvarious Arabic dialects. We also present a novel design of anArabic dialect identification system using Hidden Markov Models(HMM). Due to the similarities and the differences between Arabicdialects, we build a ergodic HMM that has two types of states; oneof them represents the common sounds across Arabic dialects, whilethe other represents the unique sounds of the specific dialect. Wetie the common states across all models since they share the samesounds. We focus only on two major dialects: Egyptian and theGulf. An improved initialization process is used to achieve betterArabic dialect identification. Moreover, we utilize many differentcombinations of speech features related to MFCC such as timederivatives, energy, and the Shifted Delta Cepstra in training andtesting the system. We present a detailed comparison of theperformance of our Arabic dialect identification system using thedifferent combinations. The best result of the Arabic dialectidentification system is 96.67\% correct identification

The Effect of Speech Elicitation Method on Second Language Phonemic Accuracy

The present study, a One-Group Posttest-Only Repeated-Measures Design, examined the effect of speech elicitation method on second language (L2) phonemic accuracy of high functional load initial phonemes found in frequently occurring nouns in American English. This effect was further analyzed by including the variable of first language (L1) to determine if L1 moderated any effects found. The data consisted of audio recordings of 61 adult English learners (ELs) enrolled in English for Academic Purposes (EAP) courses at a large, public, post-secondary institution in the United States. Phonemic accuracy was judged by two independent raters as either approximating a standard American English (SAE) pronunciation of the intended phoneme or not, thus a dichotomous scale, and scores were assigned to each participant in terms of the three speech elicitation methods of word reading, word repetition, and picture naming. Results from a repeated measures ANOVA test revealed a statistically significant difference in phonemic accuracy (F(1.47, 87.93) = 25.94, p = .000) based on speech elicitation method, while the two-factor mixed design ANOVA test indicated no statistically significant differences for the moderator variable of native language. However, post-hoc analyses revealed that mean scores of picture naming tasks differed significantly from the other two elicitation methods of word reading and word repetition. Moreover, the results of this study should heighten attention to the role that various speech elicitation methods, or input modalities, might play on L2 productive accuracy. Implications for practical application suggest that caution should be used when utilizing pictures to elicit specific vocabulary words–even high-frequency words–as they might result in erroneous productions or no utterance at all. These methods could inform pronunciation instructors about best teaching practices when pronunciation accuracy is the objective. Finally, the impact of L1 on L2 pronunciation accuracy might not be as important as once thought

1915-16 Xavier University Course Catalog

https://www.exhibit.xavier.edu/coursecatalog/1077/thumbnail.jp

Communications, Strategies & Performance

Communications, Strategies & Performance is a compilation of open educational resources that provides an in-depth analysis on topics ranging from public speaking to the communication process to the ethics and cultural considerations of speech and communication. Course: COM 202https://spiral.lynn.edu/ludp/1040/thumbnail.jp