Latent semantic retrieval of spoken documents over position specific posterior lattices

Chang, Hung-lin; Pan, Yaodong; Lee, Lin-Shan

doi:10.1109/slt.2008.4777896

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…Subword-based query expansion, in which suitable subword sequences are found in the subword-based lattices for query expansion, can address this problem to some extent [274], [287]- [290]. However, Fig.…”

Section: E Query Expansion With Acoustic Patternsmentioning

confidence: 99%

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Lee

Glass

Lee

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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100

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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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Section: E Query Expansion With Acoustic Patternsmentioning

confidence: 99%

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Lee

Glass

Lee

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

100

View full text Add to dashboard Cite

show abstract

“…Considering that the perfect ASR system is not yet a shortterm perspective, many recent studies on SDR focus on errortolerant indexing methods, based on word-lattices or N-Best list representations of ASR outputs [5,6], indexing strategies [7,8,9] and the handling of out-of-vocabulary words.…”

Section: Introductionmentioning

confidence: 99%

A segment-level confidence measure for Spoken Document Retrieval

Senay

Linarès

Lecouteux

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a semantic confidence measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) confidence measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER, by simulating a classical SDR usage scenario : users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.

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Modeling query-document dependencies with topic language models for information retrieval

2015

Information Sciences

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Latent semantic retrieval of spoken documents over position specific posterior lattices

Cited by 9 publications

References 9 publications

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

A segment-level confidence measure for Spoken Document Retrieval

Modeling query-document dependencies with topic language models for information retrieval

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