Zero resource graph-based confidence estimation for open vocabulary spoken term detection

Norouzian, Atta; Rose, Richard C.; Ghalehjegh, Sina Hamidi; Jansen, Aren

doi:10.1109/icassp.2013.6639282

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…The figure-of-merit [37] of the original and fused systems were 38.7% and 43.7%, respectively-a 13% relative performance improvement from augmenting the core STD system with zero resource techniques. For details, see [39].…”

Section: Improving Abstractearch Using Lexical Discoverymentioning

confidence: 99%

A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition

Jansen¹,

Dupoux

Goldwater

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

112

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We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.

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Section: Improving Abstractearch Using Lexical Discoverymentioning

confidence: 99%

A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition

Jansen¹,

Dupoux

Goldwater

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

112

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show abstract

“…In the experiments on lectures for a course taught by a single instructor, 21.2% relative improvement for speaker independent recognition was obtained. It also yielded 13% relative improvement for a set of OOV queries on audio recordings of McGill course lectures [193] with several speakers [185], and 6.1% relative improvements on broadcast news with many speakers [184]. The graph-based approach with random walk was also shown to outperform the exemplar-based approach with examples from PRF [181].…”

Section: E Graph-based Approachmentioning

confidence: 93%

“…Another way to exploit the graph structure is using the random walk [181]- [183], [185], which does not use any labelled data. The basic idea is that the hypothesized regions (nodes) strongly connected to many other hypothesized regions (nodes) with higher/lower confidence scores on the graph should have higher/lower scores.…”

Section: E Graph-based Approachmentioning

confidence: 99%

“…It is reasonable to expect that globally considering the similarity structure among all hypothesized regions obtained in the first pass, rather than relying on the assumptions of examples in PRF, can better re-rank the hypothesized regions. This can be formulated as a problem using graph theory [180]- [183], [185]. As shown in Fig.…”

Section: E Graph-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Lee

Glass

Lee

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

101

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Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.Index Terms-Spoken content retrieval, spoken term detection, query by example, semantic retrieval, joint optimization, pseudo-relevance feedback, graph-based random walk, unsupervised acoustic pattern discovery, query expansion, interactive retrieval, summarization, key term extraction.

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“…It is observed that the original detection scores, which are normally the posterior probabilities [14] of keywords at detected locations, might not be robustly estimated in adverse conditions. Thus, various approaches has been proposed to rescore the KWS detections [15][16][17][18][19][20][21][22][23]33].…”

Section: Introductionmentioning

confidence: 99%

Rescoring Hypothesized Detections of Out-of-Vocabulary Keywords Using Subword Samples

Pham

Xiao

et al. 2016

Interspeech 2016

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Rescoring hypothesized detections, using keyword's audio samples extracted from training data, is an effective way to improve the performance of a Keyword Search (KWS) system. Unfortunately such rescoring framework cannot be applied directly to Out-of-Vocabulary (OOV) keywords since there is no sample in the training data. To address this limitation, we propose two techniques for OOV keywords in this work. The first technique generates samples for an OOV keyword by concatenating samples of its constituent subwords. The second technique splits hypothesized detections into segments, then estimates the acoustic similarities between detections and subword's samples according to the similarities between segments and these samples. The similarity scores from these two techniques are used to rescore and re-rank the list of detections returned by the automatic speech recognition (ASR) systems. The experiments show that incorporating the proposed similarity scores results in a better separation between the correct and false alarm detections than using the ASR scores alone. Furthermore, experimental results on the NIST OpenKWS15 Evaluation show that rescoring with the proposed similarity scores significantly outperforms the raw ASR scores, and other methods that do not use the similarity scores, in both Maximum Term Weighted Value (MTWV) and Mean Average Precision (MAP) metrics.

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Zero resource graph-based confidence estimation for open vocabulary spoken term detection

Cited by 8 publications

References 13 publications

A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition

A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Rescoring Hypothesized Detections of Out-of-Vocabulary Keywords Using Subword Samples

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