382 research outputs found
Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
An Investigation of Intelligibility and Lingua Franca Core Features in Indonesian Accented English
Recent approaches to teaching pronunciation of English in second or foreign language contexts have favoured the role of students’ L1 accents in the teaching and learning process with the emphasis on intelligibility and the use of English as a Lingua Franca rather than on achieving native like pronunciation. As far as English teaching in Indonesia is concerned, there is limited information on the intelligibility of Indonesian Accented English, as well as insufficient guidance on key pronunciation features for effective teaching. This research investigates features of Indonesian Accented English and critically assesses the intelligibility of different levels of Indonesian Accented English.English Speech data were elicited from 50 Indonesian speakers using reading texts. Key phonological features of Indonesian Accented English were investigated through acoustic analysis involving spectrographic observation using Praat Speech Analysis software. The intelligibility of different levels of Indonesian Accented English was measured using a transcription task performed by 24 native and non-native English listeners. The overall intelligibility of each accent was measured by examining the correctness of the transcriptions. The key pronunciation features which caused intelligibility failure were identified by analysing the incorrect transcriptions.The analysis of the key phonological features of Indonesian Accented English showed that while there was some degree of regularity in the production of vowel duration and consonant clusters, more individual variations were observed in segmental features particularly in the production of consonants /v, z, ʃ/ which are absent in the Indonesian phonemic inventory. The results of the intelligibility analysis revealed that although light and moderate accented speech data were significantly more intelligible than the heavier accented speech data, the native and non-native listeners did not have major problems with the intelligibility of Indonesian Accented English across the different accent levels. The analysis of incorrect transcriptions suggested that intelligibility failures were associated more with combined phonological miscues rather than a single factor. These results indicate that while Indonesian Accented English can be used effectively in international communication, it can also inform English language teaching in Indonesia
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Hearing the message and seeing the messenger: The role of talker information in spoken language comprehension
The acoustic signal consists of various layers of information that we often process unconsciously. Most importantly, they contain both linguistic and indexical information, which are the two fundamental components within the sound input. Even though the meaning of the word does not change when spoken by multiple speakers, the same word never sounds exactly the same. That is because individuals introduce all kinds of variation to the speech input. Hence, through segmental and suprasegmental information, listeners can discern the nativeness (native vs. non-native) of the talker and the age of the talker (adult vs. child). Both non-native talkers and child talkers deviate from the standard norms of pronunciation of native adults and show variation both within and between talkers. The main difference between non-native adults and native children is that, for non-native talkers, variation is driven by their native language, meaning that the phonological structures of their native language interact with their second language; therefore, they maintain a foreign accent. For children, however, variation is driven by development, such that children's competencies in their motor skills depend on their current stage of language development. While there has been extensive research on foreign-accented speech, there is little knowledge about child speech. Especially the processing of child speech has only been investigated by a few studies so far. Hence, the central question of the dissertation is "What is the role of talker information in spoken language comprehension?" This question was investigated from three distinct angles: The first project examined talker information from an auditory-only perspective, the second project investigated talker information from an audio-visual perspective, and the third project studied the impact of talker information on listeners' credibility ratings in the socio-linguistic context
CLARIN
The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium
The Phonetic Specificity of British English-Learning Infants’ Word Form Recognition in Their First Year of Life
Consonants and vowels have been proposed to have distinct functions in speech perception: a consonant bias for lexical processing and a vowel bias for syntactic/prosodic processing (Nespor et al., 2003). Research in adults has consistently demonstrated that consonants have a privileged role in various lexical-level experiments across most languages. However, cross-linguistic differences have been found in the developmental trajectory of the consonant bias. For example, whilst French-learning infants display a consonant advantage in lexical processing tasks by their first birthday (e.g., Poltrock & Nazzi, 2015), British English-learning infants show an equal sensitivity to consonants and vowels until the age of 30 months (e.g., Floccia et al., 2014). Although the lexical and/or the acoustic-phonetic properties of an infant’s native language have been hypothesised to explain such variations, additional cross-linguistic tests of the consonant bias and its potential links to these factors are required. The present thesis explored this by using two experimental paradigms to further examine the phonetic specificity of British English-learning infants’ word form recognition at the onset of lexical acquisition. Experiments 1 to 3 established an equal preference for consonant and vowel mispronunciations of familiar word forms, presented either in isolation or in list form, in 5-, 11-, and 12-month-old infants using the head-turn preference procedure. Experiments 4 and 5 used an eye-tracking methodology to measure whether the congruent presentation of audio and visual speech signals led to a consonant bias in 12-month-olds’ word form recognition. An audiovisual benefit was found, with infants discriminating between phonetic mispronunciations, but only when they viewed a speaker articulate alterations of a single familiar word form. Additionally, neither acoustic factors (Experiment 1) nor lexical factors (Experiments 2 to 5) were found to influence infants’ preferences. Together, the results of this thesis provide further evidence that initial lexical processes vary cross linguistically
Methods in Contemporary Linguistics
The present volume is a broad overview of methods and methodologies in linguistics, illustrated with examples from concrete research. It collects insights gained from a broad range of linguistic sub-disciplines, ranging from core disciplines to topics in cross-linguistic and language-internal diversity or to contributions towards language, space and society. Given its critical and innovative nature, the volume is a valuable source for students and researchers of a broad range of linguistic interests
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