A computational model for studying L1’s effect on L2 speech learning

abstract: Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. To test the hypotheses, this study comes up with a computational model to analyze the accented speech properties in both segmental (short-term speech measurements on short-segment or phoneme level) and suprasegmental (long-term speech measurements on word, long-segment, or sentence level) feature space. The benefit of using a computational model is that it enables quantitative analysis of L1's effect on accent in terms of different phonological properties. The core parts of this computational model are feature extraction schemes to extract pronunciation and prosody representation of accented speech based on existing techniques in speech processing field. Correlation analysis on both segmental and suprasegmental feature space is conducted to look into the relationship between acoustic measurements related to L1s and perceived accentedness across several L1s. Multiple regression analysis is employed to investigate how the L1's effect impacts the perception of foreign accent, and how accented speech produced by speakers from different L1s behaves distinctly on segmental and suprasegmental feature spaces. Results unveil the potential application of the methodology in this study to provide quantitative analysis of accented speech, and extend current studies in L2 speech learning theory to large scale. Practically, this study further shows that the computational model proposed in this study can benefit automatic accentedness evaluation system by adding features related to speakers' L1s.Dissertation/ThesisDoctoral Dissertation Speech and Hearing Science 201

Clearing the transcription hurdle in dialect corpus building : the corpus of Southern Dutch dialects as case-study

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve

Teaching American English Pronunciation in a Spanish Speaking Context: A Guide for EFL Teachers in Chile

This field project addresses the lack of education surrounding pronunciation teaching, and it specifically responds to the opportunities available within English as a Foreign Language settings. This project is especially relevant to English language teachers who would like to study pronunciation teaching and its history. The objective of this research is to inform educators of the validity of teaching pronunciation and to teach educators effective strategies that can be used. Theories of Contrastive Analysis and Error Analysis form the foundation of this project, as well as theories of Interlanguage and Markedness. The literature review includes extensive research into the subject matter of pronunciation teaching and Contrastive Analysis. The project is a video-lecture series to support the pronunciation instruction for native Spanish speakers. The Contrastive Analysis is between English and Spanish, and the video-lecture series was made for a specifically Chilean audience

Clearing the Transcription Hurdle in Dialect Corpus Building:The Corpus of Southern Dutch Dialects as Case Study

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve.</p

A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature representations. Using automatic speech recognition (ASR) as a downstream task, we analyze the effect of model size, training objectives, and model architecture on the models' performance as a feature extractor for a set of topologically diverse corpora. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations using deep generalized canonical correlation analysis. Results show the contrastive loss in the wav2vec2.0 objective facilitates more effective cross-lingual feature extraction. There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings. The proposed metric is an effective indicator of the quality of the representations and can be useful for model selection.Comment: 12 pages, 5 figures, 4 table

Exploring the effects of accent on cognitive processes: behavioral and electrophysiological insights

167 p.Previous research has found that speaker accent can have an impact on a range of offline and online cognitive processes (Baus, Bas, Calabria, & Costa, 2017; McAleer, Todorov, & Belin, 2014; Stevenage, Clarke, & McNeill, 2012; Sporer, 2001). Indeed, previous studies show that there are differences in native and non-native speech processing (Lev-Ari, 2018). Processing foreign-accented speech requires the listener to adapt to an extra range of variability, suggesting that there may be an increase in the amount of attentional and cognitive resources that are needed to successfully interpret the speech signal of a foreign-accented speaker. However, less is known about the differences between processing native and dialectal accents. Is dialectal processing more similar to foreign or native speech? To address this, two theories have been proposed (Clarke & Garrett, 2004; Floccia et al, 2009). Previous studies have contributed to the plausibility of both hypotheses and importantly for the purposes of this project, previous electroencephalography experiments exploring the question have mainly used sentences as material. More studies are needed to elucidate whether foreign accent is processed uniquely from all types of native speech (both native and dialectal accents) or whether dialectal accent is treated differently from native accent, despite both being native speech variations. Accordingly, the central aim of this dissertation is to further investigate processing mechanisms of speech accent across different levels of linguistic analysis using evidence from both behavioral and electrophysiological experiments. An additional aim of this project was to look at the effects of accent on information retention. In addition to fluctuations in attentional demands, it seems that non-native accent can lead to differences in the depth of listeners¿ memory encoding (Atkinson et al., 2005). This project further aimed to study how changing the accent of the information delivered may affect how well people remember the information received. Three experiments were carried out to investigate accent processing, results and future directions are discussed

Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

Towards Zero-shot Learning for Automatic Phonemic Transcription

Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemic transcription tools. Fortunately, multilingual acoustic modeling provides a solution given limited audio training data. A more challenging problem is to build phonemic transcribers for languages with zero training data. The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes. In this work, we address this problem by adopting the idea of zero-shot learning. Our model is able to recognize unseen phonemes in the target language without any training data. In our model, we decompose phonemes into corresponding articulatory attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over articulatory attributes, and then compute phoneme distributions with a customized acoustic model. We evaluate our model by training it using 13 languages and testing it using 7 unseen languages. We find that it achieves 7.7% better phoneme error rate on average over a standard multilingual model.Comment: AAAI 202