5,646 research outputs found
Topic Identification for Speech without ASR
Modern topic identification (topic ID) systems for speech use automatic
speech recognition (ASR) to produce speech transcripts, and perform supervised
classification on such ASR outputs. However, under resource-limited conditions,
the manually transcribed speech required to develop standard ASR systems can be
severely limited or unavailable. In this paper, we investigate alternative
unsupervised solutions to obtaining tokenizations of speech in terms of a
vocabulary of automatically discovered word-like or phoneme-like units, without
depending on the supervised training of ASR systems. Moreover, using automatic
phoneme-like tokenizations, we demonstrate that a convolutional neural network
based framework for learning spoken document representations provides
competitive performance compared to a standard bag-of-words representation, as
evidenced by comprehensive topic ID evaluations on both single-label and
multi-label classification tasks.Comment: 5 pages, 2 figures; accepted for publication at Interspeech 201
A Comparison of Normalization Techniques Applied to Latent Space Representations for Speech Analytics
International audienceIn the context of noisy environments, Automatic Speech Recognition (ASR) systems usually produce poor transcription quality which also negatively impact performance of speech analyt-ics. Various methods have then been proposed to compensate the bad effect of ASR errors, mainly by projecting transcribed words in an abstract space. In this paper, we seek to identify themes from dialogues of telephone conversation services using latent topic-spaces estimated from a latent Dirichlet allocation (LDA). As an outcome, a document can be represented with a vector containing probabilities to be associated to each topic estimated with LDA. This vector should nonetheless be normalized to condition document representations. We propose to compare the original LDA vector representation (without normalization) with two normalization approaches, the Eigen Factor Radial (EFR) and the Feature Warping (FW) methods, already successfully applied in speaker recognition field, but never compared and evaluated in the context of a speech analytic task. Results show the interest of these normalization techniques for theme identification tasks using automatic transcriptions The EFR normalization approach allows a gain of 3.67 and 3.06 points respectively in comparison to the absence of normalization and to the FW normalization technique
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Many uses, many annotations for large speech corpora: Switchboard and TDT as case studies
This paper discusses the challenges that arise when large speech corpora
receive an ever-broadening range of diverse and distinct annotations. Two case
studies of this process are presented: the Switchboard Corpus of telephone
conversations and the TDT2 corpus of broadcast news. Switchboard has undergone
two independent transcriptions and various types of additional annotation, all
carried out as separate projects that were dispersed both geographically and
chronologically. The TDT2 corpus has also received a variety of annotations,
but all directly created or managed by a core group. In both cases, issues
arise involving the propagation of repairs, consistency of references, and the
ability to integrate annotations having different formats and levels of detail.
We describe a general framework whereby these issues can be addressed
successfully.Comment: 7 pages, 2 figure
Follow-up question handling in the IMIX and Ritel systems: A comparative study
One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it
Automated speech and audio analysis for semantic access to multimedia
The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery
Acoustic unit discovery (AUD) is a process of automatically identifying a
categorical acoustic unit inventory from speech and producing corresponding
acoustic unit tokenizations. AUD provides an important avenue for unsupervised
acoustic model training in a zero resource setting where expert-provided
linguistic knowledge and transcribed speech are unavailable. Therefore, to
further facilitate zero-resource AUD process, in this paper, we demonstrate
acoustic feature representations can be significantly improved by (i)
performing linear discriminant analysis (LDA) in an unsupervised self-trained
fashion, and (ii) leveraging resources of other languages through building a
multilingual bottleneck (BN) feature extractor to give effective cross-lingual
generalization. Moreover, we perform comprehensive evaluations of AUD efficacy
on multiple downstream speech applications, and their correlated performance
suggests that AUD evaluations are feasible using different alternative language
resources when only a subset of these evaluation resources can be available in
typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201
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