DocumentCode :
80642
Title :
Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval
Author :
Lin-Shan Lee ; Glass, James ; Hung-yi Lee ; Chun-an Chan
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume :
23
Issue :
9
fYear :
2015
fDate :
Sept. 2015
Firstpage :
1389
Lastpage :
1420
Abstract :
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 que- y, 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.
Keywords :
acoustic signal processing; content-based retrieval; indexing; interactive systems; multimedia systems; speech recognition; ASR; acoustic level; audio; cascading automatic speech recognition; interactive retrieval; lattice format; multimedia content; phonemes; retrieved object presentation; signal matching; speech signals; spoken content indexing; spoken content retrieval; spoken queries; text information retrieval; Accuracy; Acoustics; Indexing; Multimedia communication; Speech; Speech processing; Speech recognition; Spoken content retrieval; graph-based random walk; interactive retrieval; joint optimization; key term extraction; pseudo-relevance feedback; query by example; query expansion; semantic retrieval; spoken term detection; summarization; unsupervised acoustic pattern discovery;
fLanguage :
English
Journal_Title :
Audio, Speech, and Language Processing, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
2329-9290
Type :
jour
DOI :
10.1109/TASLP.2015.2438543
Filename :
7114229
Link To Document :
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