Scoping a vision for formative e-assessment: a project report for JISC

Assessment is an integral part of teaching and learning. If the relationship between teaching and learning were causal, i. e. if students always mastered the intended learning outcomes of a particular sequence of instruction, assessment would be superfluous. Experience and research suggest this is not the case: what is learnt can often be quite different from what is taught. Formative assessment is motivated by a concern with the elicitation of relevant information about student understanding and / or achievement, its interpretation and an exploration of how it can lead to actions that result in better learning. In the context of a policy drive towards technology-enhanced approaches to teaching and learning, the question of the role of digital technologies is key and it is the latter on which this project particularly focuses. The project and its deliverables have been informed by recent and relevant literature, in particular recent work by Black andIn this work, they put forward a framework which suggests that assessment for learning their term for formative assessment can be conceptualised as consisting of a number of aspects and five keystrategies. The key aspects revolve around the where the learner is going, where the learner is right now and how she can get there and examines the role played by the teacher, peers and the learner. Language: English Keywords: assessments, case studies, design patterns, e-assessmen

Phoneme duration modelling for speaker verification

Higher-level features are considered to be a potential remedy against transmission line and cross-channel degradations, currently some of the biggest problems associated with speaker verification. Phoneme durations in particular are not altered by these factors; thus a robust duration model will be a particularly useful addition to traditional cepstral based speaker verification systems. In this dissertation we investigate the feasibility of phoneme durations as a feature for speaker verification. Simple speaker specific triphone duration models are created to statistically represent the phoneme durations. Durations are obtained from an automatic hidden Markov model (HMM) based automatic speech recognition system and are modeled using single mixture Gaussian distributions. These models are applied in a speaker verification system (trained and tested on the YOHO corpus) and found to be a useful feature, even when used in isolation. When fused with acoustic features, verification performance increases significantly. A novel speech rate normalization technique is developed in order to remove some of the inherent intra-speaker variability (due to differing speech rates). Speech rate variability has a negative impact on both speaker verification and automatic speech recognition. Although the duration modelling seems to benefit only slightly from this procedure, the fused system performance improvement is substantial. Other factors known to influence the duration of phonemes are incorporated into the duration model. Utterance final lengthening is known be a consistent effect and thus “position in sentence” is modeled. “Position in word” is also modeled since triphones do not provide enough contextual information. This is found to improve performance since some vowels’ duration are particularly sensitive to its position in the word. Data scarcity becomes a problem when building speaker specific duration models. By using information from available data, unknown durations can be predicted in an attempt to overcome the data scarcity problem. To this end we develop a novel approach to predict unknown phoneme durations from the values of known phoneme durations for a particular speaker, based on the maximum likelihood criterion. This model is based on the observation that phonemes from the same broad phonetic class tend to co-vary strongly, but that there is also significant cross-class correlations. This approach is tested on the TIMIT corpus and found to be more accurate than using back-off techniques.Dissertation (MEng)--University of Pretoria, 2009.Electrical, Electronic and Computer Engineeringunrestricte

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Security and Privacy Problems in Voice Assistant Applications: A Survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

Applying source separation to music

International audienceSeparation of existing audio into remixable elements is very useful to repurpose music audio. Applications include upmixing video soundtracks to surround sound (e.g. home theater 5.1 systems), facilitating music transcriptions, allowing better mashups and remixes for disk jockeys, and rebalancing sound levels on multiple instruments or voices recorded simultaneously to a single track. In this chapter, we provide an overview of the algorithms and approaches designed to address the challenges and opportunities in music. Where applicable, we also introduce commonalities and links to source separation for video soundtracks, since many musical scenarios involve video soundtracks (e.g. YouTube recordings of live concerts, movie sound tracks). While space prohibits describing every method in detail, we include detail on representative music‐specific algorithms and approaches not covered in other chapters. The intent is to give the reader a high‐level understanding of the workings of key exemplars of the source separation approaches applied in this domain