Indexing and retrieval of broadcast news

Renals, Steve; Abberley, Dave; Kirby, David A.; Robinson, Tony

doi:10.1016/s0167-6393(00)00020-0

Cited by 40 publications

(27 citation statements)

References 30 publications

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“…However, in a confusion network, arcs are aligned to columns as in Fig. 1 (b) and the weight of each arc has already been normalized so that the sum of weights in each column becomes 1 …”

Section: Indexing With Confusion Networkmentioning

confidence: 99%

“…Recently, spoken document retrieval (SDR) has intensively been investigated in the SDR track of Text REtrieval Conference (TREC) [1], in which the task is to retrieve broadcast news stories relevant to some specific keywords. Usually each document is previously transcribed by automatic speech recognition (ASR), and indexed with the transcribed text.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Open-Vocabulary Spoken Utterance Retrieval using Confusion Networks

Hori¹,

Hetherington²,

Hazen³

et al. 2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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This paper presents a novel approach to open-vocabulary spoken utterance retrieval using confusion networks. If out-of-vocabulary (OOV) words are present in queries and the corpus, word-based indexing will not be sufficient. For this problem, we apply phone confusion networks and combine them with word confusion networks. With this approach, we can generate a more compact index table that enables robust keyword matching compared with typical lattice-based methods. In the retrieval experiments with speech recordings in MIT lecture corpus, our method using phone confusion networks outperformed lattice-based methods especially for OOV queries.

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“…However, in a confusion network, arcs are aligned to columns as in Fig. 1 (b) and the weight of each arc has already been normalized so that the sum of weights in each column becomes 1 …”

Section: Indexing With Confusion Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open-Vocabulary Spoken Utterance Retrieval using Confusion Networks

Hori¹,

Hetherington²,

Hazen³

et al. 2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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show abstract

“…These multimedia documents also give rise to other applications such as multimodal tracking of objects/humans [12]. Furthermore, as the quantity of such archived documents grows, it becomes important to develop multimedia document retrieval systems [21,24] to find relevant documents based not only on their textual content but also on their joint visual and audio content.…”

Section: Some Applicationsmentioning

confidence: 99%

Multi Channel Sequence Processing

Bengio

Bourlard

2005

Lecture Notes in Computer Science

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Abstract. This paper summarizes some of the current research challenges arising from multichannel sequence processing. Indeed, multiple real life applications involve simultaneous recording and analysis of multiple information sources, which may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. Some of these problems can already be tackled by one of the many statistical approaches towards sequence modeling. However, several challenging research issues are still open, such as taking into account asynchrony and correlation between several feature streams, or handling the underlying growing complexity. In this framework, we discuss here two novel approaches, which recently started to be investigated with success in the context of large multimodal problems. These include the asynchronous HMM, providing a principled approach towards the processing of multiple feature streams, and the layered HMM approach, providing a good formalism for decomposing large and complex (multi-stream) problems into layered architectures. As briefly reported here, combination of these two approaches yielded successful results on several multi-channel tasks, ranging from audio-visual speech recognition to automatic meeting analysis. 2 IDIAP-RR 05-04

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“…Keyword spotting, a special branch in continuous speech recognition, has appeared on data from telephone speech [1], air travel information [2], broadcast news task [3]. One problem of state-of-the-art keyword spotting systems is that the relevant keywords are out of vocabulary in many applications, for example when searching names, places, acronyms, etc.…”

Section: Introductionmentioning

confidence: 99%