Intelligibility, accentedness, and attitudes in English as a lingua Franca

&nbsp;A number of contributions stem from this study- shared first language and typology between NNSs do not have a positive impact on the intelligibility or accentedness in English as a Lingua Franca. The phonology of L2 speech, perceived identity, and identity transformations underline emotional attitudes towards convergent Englishes in ELF.<br /

At the interface: Dynamic interactions of explicit and implicit language knowledge.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139748/1/AttheInterface.pd

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Noise-Robust Speech Recognition Using Deep Neural Network

Ph.DDOCTOR OF PHILOSOPH

Automatic Screening of Childhood Speech Sound Disorders and Detection of Associated Pronunciation Errors

Speech disorders in children can affect their fluency and intelligibility. Delay in their diagnosis and treatment increases the risk of social impairment and learning disabilities. With the significant shortage of Speech and Language Pathologists (SLPs), there is an increasing interest in Computer-Aided Speech Therapy tools with automatic detection and diagnosis capability. However, the scarcity and unreliable annotation of disordered child speech corpora along with the high acoustic variations in the child speech data has impeded the development of reliable automatic detection and diagnosis of childhood speech sound disorders. Therefore, this thesis investigates two types of detection systems that can be achieved with minimum dependency on annotated mispronounced speech data. First, a novel approach that adopts paralinguistic features which represent the prosodic, spectral, and voice quality characteristics of the speech was proposed to perform segment- and subject-level classification of Typically Developing (TD) and Speech Sound Disordered (SSD) child speech using a binary Support Vector Machine (SVM) classifier. As paralinguistic features are both language- and content-independent, they can be extracted from an unannotated speech signal. Second, a novel Mispronunciation Detection and Diagnosis (MDD) approach was introduced to detect the pronunciation errors made due to SSDs and provide low-level diagnostic information that can be used in constructing formative feedback and a detailed diagnostic report. Unlike existing MDD methods where detection and diagnosis are performed at the phoneme level, the proposed method achieved MDD at the speech attribute level, namely the manners and places of articulations. The speech attribute features describe the involved articulators and their interactions when making a speech sound allowing a low-level description of the pronunciation error to be provided. Two novel methods to model speech attributes are further proposed in this thesis, a frame-based (phoneme-alignment) method leveraging the Multi-Task Learning (MTL) criterion and training a separate model for each attribute, and an alignment-free jointly-learnt method based on the Connectionist Temporal Classification (CTC) sequence to sequence criterion. The proposed techniques have been evaluated using standard and publicly accessible adult and child speech corpora, while the MDD method has been validated using L2 speech corpora

Individual Differences in Speech Production and Perception

Inter-individual variation in speech is a topic of increasing interest both in human sciences and speech technology. It can yield important insights into biological, cognitive, communicative, and social aspects of language. Written by specialists in psycholinguistics, phonetics, speech development, speech perception and speech technology, this volume presents experimental and modeling studies that provide the reader with a deep understanding of interspeaker variability and its role in speech processing, speech development, and interspeaker interactions. It discusses how theoretical models take into account individual behavior, explains why interspeaker variability enriches speech communication, and summarizes the limitations of the use of speaker information in forensics

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination

The effect of study abroad experience and working memory on Chinese-English consecutive interpreting performance

This thesis investigates how study abroad experience (SAE) and working memory (WM) influence interpreting performance. Using a second language (L2) is cognitively demanding because it involves activation of a new language and the inhibition of the first language (L1). This is a general issue with all bilinguals, who have to suppress or control whichever language is currently not in use. As a special group of bilinguals, interpreters are expected to efficiently switch between the two languages by analysing input sound signals, extracting meaning, transforming, storing and retrieving the message in the input language, and then retrieving the lexicon in the target language that will be appropriate for expressing that message, (re)formulating it and finally conveying it in the target language. Moreover, some or all of these operations take place in parallel, and this multi-tasking heavily taxes interpreters’ WM. The quality of interpreting performance is known to correlate with several variables, such as language proficiency, duration of training, and interpreting experience. One factor that has received little research attention is the effect of overseas experience: Does studying in a target-language environment benefit interpreting performance? Language learners, including interpreting students, are often advised to study abroad, but the benefits of this experience, especially for interpreters, is not well understood. Taking an interdisciplinary approach, the present thesis examines the relationship between SAE, WM and interpreting performance. The main research questions examine whether students with SAE outperform those without such an experience in consecutive interpreting (CI), and how WM may be involved. The results show that students with SAE surpassed their non-SAE counterparts in word translation efficiency, L2 fluency and L2 grammatical accuracy. A similar trend was observed in study abroad participants’ overall CI performance from L2 to L1. It is worth noting that the tendency was independent of participants’ WM. Concerning WM, the results indicate that it was strongly correlated with interpreters’ bidirectional CI performance. That is, a larger WM could help achieve a better CI output in both language directions. Taken together, these findings suggest that two factors turn out to significantly influence CI performance, namely, prolonged and effective overseas study, and larger available WM resources. This research illustrates the importance of SAE and WM in interpreting, and sheds light on the relationships between language context, cognitive resources and interpreting performance. A better understanding of these relationships may have implications for future interpreting training and practice