The Spoken British National Corpus 2014:design, compilation and analysis

The ESRC-funded Centre for Corpus Approaches to Social Science at Lancaster University (CASS) and the English Language Teaching group at Cambridge University Press (CUP) have compiled a new, publicly-accessible corpus of spoken British English from the 2010s, known as the Spoken British National Corpus 2014 (Spoken BNC2014). The 11.5 million-word corpus, gathered solely in informal contexts, is the first freely-accessible corpus of its kind since the spoken component of the original British National Corpus (the Spoken BNC1994), which, despite its age, is still used as a proxy for present-day English in research today. This thesis presents a detailed account of each stage of the Spoken BNC2014’s construction, including its conception, design, transcription, processing and dissemination. It also demonstrates the research potential of the corpus, by presenting a diachronic analysis of ‘bad language’ in spoken British English, comparing the 1990s to the 2010s. The thesis shows how the research team struck a delicate balance between backwards compatibility with the Spoken BNC1994 and optimal practice in the context of compiling a new corpus. Although comparable with its predecessor, the Spoken BNC2014 is shown to represent innovation in approaches to the compilation of spoken corpora. This thesis makes several useful contributions to the linguistic research community. The Spoken BNC2014 itself should be of use to many researchers, educators and students in the corpus linguistics and English language communities and beyond. In addition, the thesis represents an example of good practice with regards to academic collaboration with a commercial stakeholder. Thirdly, although not a ‘user guide’, the methodological discussions and analysis presented in this thesis are intended to help the Spoken BNC2014 to be as useful to as many people, and for as many purposes, as possible

A systematic review of speech recognition technology in health care

BACKGROUND To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.Funding for this study was provided by the University of Western Sydney. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners

What Do Caregivers Tell Us about Infant Babbling?

Phonetic repertoires in babbling are an important marker of prelinguistic development. Typical phonetic development, however, is difficult to identify given variability within and across infants. Prior to 18 months of infant age, caregiver report of prelinguistic vocal development is often an important part of clinical practice for early intervention. As a first step toward understanding the utility of caregiver report of babbling, the purpose of this exploratory study was to determine how the phonetic makeup of sounds reported by caregivers in infant babbling would develop, in particular comparison to markedness theory and established norms. In a longitudinal design, caregiver report was tracked through weekly interviews from 7 to 18 months of infant age (N = 15). Reports were phonetically transcribed and examined in terms of the number of utterances; place, manner, and voicing for consonants; and tongue position for vowels. In general, the number of utterances and phonetic segments reported by caregivers increased significantly with infant age (p < .05) and phonetic feature patterns were similar to what one would expect in the vocal development of English-learning infants. Results support the notion that caregiver report of infant vocalizations may provide a valuable means for describing early infant babbling development

Building the Arabic Learner Corpus and a System for Arabic Error Annotation

Recent developments in learner corpora have highlighted the growing role they play in some linguistic and computational research areas such as language teaching and natural language processing. However, there is a lack of a well-designed Arabic learner corpus that can be used for studies in the aforementioned research areas. This thesis aims to introduce a detailed and original methodology for developing a new learner corpus. This methodology which represents the major contribution of the thesis includes a combination of resources, proposed standards and tools developed for the Arabic Learner Corpus project. The resources include the Arabic Learner Corpus, which is the largest learner corpus for Arabic based on systematic design criteria. The resources also include the Error Tagset of Arabic that was designed for annotating errors in Arabic covering 29 types of errors under five broad categories. The Guide on Design Criteria for Learner Corpus is an example of the proposed standards which was created based on a review of previous work. It focuses on 11 aspects of corpus design criteria. The tools include the Computer-aided Error Annotation Tool for Arabic that provides some functions facilitating error annotation such as the smart-selection function and the auto-tagging function. Additionally, the tools include the ALC Search Tool that is developed to enable searching the ALC and downloading the source files based on a number of determinants. The project was successfully able to recruit 992 people including language learners, data collectors, evaluators, annotators and collaborators from more than 30 educational institutions in Saudi Arabia and the UK. The data of the Arabic Learner Corpus was used in a number of projects for different purposes including error detection and correction, native language identification, Arabic analysers evaluation, applied linguistics studies and data-driven Arabic learning. The use of the ALC highlights the extent to which it is important to develop this project

Phone Merging for Code-switched Speech Recognition

International audienceSpeakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of code-switched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare base-line system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal

Characterizing Spoken Discourse in Individuals with Parkinson Disease Without Dementia

Background: The effects of disease (PD) on cognition, word retrieval, syntax, and speech/voice processes may interact to manifest uniquely in spoken language tasks. A handful of studies have explored spoken discourse production in PD and, while not ubiquitously, have reported a number of impairments including: reduced words per minute, reduced grammatical complexity, reduced informativeness, and increased verbal disruption. Methodological differences have impeded cross-study comparisons. As such, the profile of spoken language impairments in PD remains ambiguous. Method: A cross-genre, multi-level discourse analysis, prospective, cross-sectional between groups study design was conducted with 19 PD participants (Mage = 70.74, MUPDRS-III = 30.26) and 19 healthy controls (Mage = 68.16) without dementia. The extensive protocol included a battery of cognitive, language, and speech measures in addition to four discourse tasks. Two tasks each from two discourse genres (picture sequence description; story retelling) were collected. Discourse samples were analysed using both microlinguistic and macrostructural measures. Discourse variables were collapsed statistically to a primal set of variables used to distinguish the spoken discourse of PD vs. controls. Results: Participants with PD differed significantly from controls along a continuum of productivity, grammar, informativeness, and verbal disruption domains including total words F(1,36) = 3.87, p = .06; words/minute F(1,36) = 7.74, p = .01 , % grammatical utterances F(1,36) = 11.92, p = .001, total CIUs F(1,36) = 13.30, p = .001, % CIUs (Correct Information Units) F(1,36) = 9.35, p = .004, CIUs/minute F(1,36) = 14.06, p = .001, and verbal disruptions/100 words F(1,36) = 3.87, p = .06 (α = .10). Discriminant function analyses showed that optimally weighted discourse variables discriminated the spoken discourse of PD vs. controls with 81.6% sensitivity and 86.8% specificity. For both discourse genres, discourse performance showed robust, positive, correlations with global cognition. In PD (picture sequence description), more impaired discourse performance correlated significantly with more severe motor impairment, more advanced disease staging, and higher doses of PD medications. Conclusions: The spoken discourse in PD without dementia differs significantly and predictably from controls. Results have both research and clinical implications

Incorporating Weak Statistics for Low-Resource Language Modeling

Automatic speech recognition (ASR) requires a strong language model to guide the acoustic model and favor likely utterances. While many tasks enjoy billions of language model training tokens, many domains which require ASR do not have readily available electronic corpora.The only source of useful language modeling data is expensive and time-consuming human transcription of in-domain audio. This dissertation seeks to quickly and inexpensively improve low-resource language modeling for use in automatic speech recognition. This dissertation first considers efficient use of non-professional human labor to best improve system performance, and demonstrate that it is better to collect more data, despite higher transcription error, than to redundantly transcribe data to improve quality. In the process of developing procedures to collect such data, this work also presents an efficient rating scheme to detect poor transcribers without gold standard data. As an alternative to this process, automatic transcripts are generated with an ASR system and explore efficiently combining these low-quality transcripts with a small amount of high quality transcripts. Standard n-gram language models are sensitive to the quality of the highest order n-gram and are unable to exploit accurate weaker statistics. Instead, a log-linear language model is introduced, which elegantly incorporates a variety of background models through MAP adaptation. This work introduces marginal class constraints which effectively capture knowledge of transcriber error and improve performance over n-gram features. Finally, this work constrains the language modeling task to keyword search of words unseen in the training text. While overall system performance is good, these words suffer the most due to a low probability in the language model. Semi-supervised learning effectively extracts likely n-grams containing these new keywords from a large corpus of audio. By using a search metric that favors recall over precision, this method captures over 80% of the potential gain

Automatic Speech Recognition for Documenting Endangered First Nations Languages

Automatic speech recognition (ASR) for low-resource languages is an active field of research. Over the past years with the advent of deep learning, impressive achievements have been reported using minimal resources. As many of the world’s languages are getting extinct every year, with every dying language we lose intellect, culture, values, and tradition which generally pass down for long generations. Linguists throughout the world have already initiated many projects on language documentation to preserve such endangered languages. Automatic speech recognition is a solution to accelerate the documentation process reducing the annotation time for field linguists as well as the overall cost of the project. A traditional speech recognizer is trained on thousands of hours of acoustic data and a phonetic dictionary that includes all words from the language. End-to-End ASR systems have shown dramatic improvement for major languages. Especially, recent advancement in self-supervised representation learning which takes advantage of large corpora of untranscribed speech data has become the state-of-the-art for speech recognition technology. However, for resource-constrained languages, the technology is not tested in depth. In this thesis, we explore both traditional methods of ASR and state-of-the-art end-to-end systems for modeling a critically endangered Athabascan language known as Upper Tanana. In our first approach, we investigate traditional models with a comparative study on feature selection and a performance comparison with deep hybrid models. With limited resources at our disposal, we build a working ASR system based on a grapheme-to-phoneme (G2P) phonetic dictionary. The acoustic model can also be used as a separate forced alignment tool for the automatic alignment of training data. The results show that the GMM-HMM methods outperform deep hybrid models in low-resource acoustic modeling. In our second approach, we propose using Domain-adapted Cross-lingual Speech Recognition (DA-XLSR) for an ASR system, developed over the wav2vec 2.0 framework that utilizes pretrained transformer models leveraging cross lingual data for building an acoustic representation. The proposed system uses a multistage transfer learning process in order to fine tune the final model. To supplement the limited data, we compile a data augmentation strategy combining six augmentation techniques. The speech model uses Connectionist Temporal Classification (CTC) for an alignment free training and does not require any pronunciation dictionary or language model. Experiments from the second approach demonstrate that it can outperform the best traditional or end-to-end models in terms of word error rate (WER) and produce a powerful utterance level transcription. On top of that, the augmentation strategy is tested on several end-to-end models, and it provides a consistent improvement in performance. While the best proposed model can currently reduce the WER significantly, it may still require further research to completely replace the need for human transcribers