1,720,971 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Low-Latency Speech Separation Guided Diarization for Telephone Conversations
Get PDFIn this paper, we carry out an analysis on the use of speech separation
guided diarization (SSGD) in telephone conversations. SSGD performs diarization
by separating the speakers signals and then applying voice activity detection
on each estimated speaker signal. In particular, we compare two low-latency
speech separation models. Moreover, we show a post-processing algorithm that
significantly reduces the false alarm errors of a SSGD pipeline. We perform our
experiments on two datasets: Fisher Corpus Part 1 and CALLHOME, evaluating both
separation and diarization metrics. Notably, our SSGD DPRNN-based online model
achieves 11.1% DER on CALLHOME, comparable with most state-of-the-art
end-to-end neural diarization models despite being trained on an order of
magnitude less data and having considerably lower latency, i.e., 0.1 vs. 10
seconds. We also show that the separated signals can be readily fed to a speech
recognition back-end with performance close to the oracle source signals.Comment: Accepted for Presentation at IEEE Spoken Language Technology Workshop
(SLT) 202
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives
Get PDFSince the first speech recognition systems were built more than 30 years ago, improvement in voice technology has enabled applications such as smart assistants and automated customer support. Conversational intelligence of the future is expected to move beyond single-user applications of voice technologies to actively participate in human conversations, including in scenarios such as note-taking, fact-checking, or collaborative learning in peer groups. For such systems, recognizing free-flowing multi-party conversations is a crucial and challenging component that still remains unsolved. In this dissertation, we focus on this problem of speaker-attributed multi-talker speech recognition for the meeting transcription task, and propose two perspectives which result from its probabilistic formulation.
In the modular perspective, speaker-attributed transcription is performed through a pipeline of sub-tasks involving speaker diarization, target speaker extraction, and speech recognition. Our first contribution is a novel method to perform overlap-aware speaker diarization by reformulating spectral clustering as a constrained optimization problem. We also describe an algorithm to ensemble diarization outputs, and show that it can be used to either combine several overlap-aware systems, or to perform multi-channel diarization by late fusion. Once speaker segments are identified, we robustly extract single-speaker utterances from the mixture using a GPU-accelerated implementation of guided source separation. This eventually allows us to use an off-the-shelf ASR system to obtain speaker-attributed transcripts.
Since the modular approach suffers from error propagation, we propose an alternate “end-to-end” perspective on the problem. For this, we describe the Streaming Unmixing and Recognition Transducer (SURT) which extends neural transducers for multi-talker ASR by incorporating an unmixing component. We show how to train SURT models efficiently by carefully designing the network architecture, objective functions, and mixture simulation techniques. Finally, we add an auxiliary speaker branch to enable joint prediction of speaker labels synchronized with the speech tokens, and propose a novel speaker prefixing approach for ensuring label consistency through the recording. We demonstrate that training on synthetic mixtures and adapting with real data helps these models transfer well for streaming transcription of real meeting sessions
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives
Get PDFSince the first speech recognition systems were built more than 30 years ago, improvement in voice technology has enabled applications such as smart assistants and automated customer support. Conversational intelligence of the future is expected to move beyond single-user applications of voice technologies to actively participate in human conversations, including in scenarios such as note-taking, fact-checking, or collaborative learning in peer groups. For such systems, recognizing free-flowing multi-party conversations is a crucial and challenging component that still remains unsolved. In this dissertation, we focus on this problem of speaker-attributed multi-talker speech recognition for the meeting transcription task, and propose two perspectives which result from its probabilistic formulation.
In the modular perspective, speaker-attributed transcription is performed through a pipeline of sub-tasks involving speaker diarization, target speaker extraction, and speech recognition. Our first contribution is a novel method to perform overlap-aware speaker diarization by reformulating spectral clustering as a constrained optimization problem. We also describe an algorithm to ensemble diarization outputs, and show that it can be used to either combine several overlap-aware systems, or to perform multi-channel diarization by late fusion. Once speaker segments are identified, we robustly extract single-speaker utterances from the mixture using a GPU-accelerated implementation of guided source separation. This eventually allows us to use an off-the-shelf ASR system to obtain speaker-attributed transcripts.
Since the modular approach suffers from error propagation, we propose an alternate “end-to-end” perspective on the problem. For this, we describe the Streaming Unmixing and Recognition Transducer (SURT) which extends neural transducers for multi-talker ASR by incorporating an unmixing component. We show how to train SURT models efficiently by carefully designing the network architecture, objective functions, and mixture simulation techniques. Finally, we add an auxiliary speaker branch to enable joint prediction of speaker labels synchronized with the speech tokens, and propose a novel speaker prefixing approach for ensuring label consistency through the recording. We demonstrate that training on synthetic mixtures and adapting with real data helps these models transfer well for streaming transcription of real meeting sessions
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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