Lattice Indexing for Spoken Term Detection

Can, Doğan; Saraçlar, Murat

doi:10.1109/tasl.2011.2134087

Cited by 128 publications

(62 citation statements)

References 17 publications

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“…To do so, these lattices are processed using the lattice indexing technique described in [99] so that the lattices of all the utterances in the search collection are converted from individual weighted finite state transducers to a single generalized factor transducer structure in which the start-time, end-time, and lattice posterior probability of each word token are stored as three-dimensional costs. This factor transducer is actually an inverted index of all word sequences seen in the lattices.…”

Section: Word-based Std Systemmentioning

confidence: 99%

ALBAYZIN 2016 spoken term detection evaluation: an international open competitive evaluation in Spanish

Tejedor

Toledano

López-Otero

et al. 2017

J AUDIO SPEECH MUSIC PROC.

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Within search-on-speech, Spoken Term Detection (STD) aims to retrieve data from a speech repository given a textual representation of a search term. This paper presents an international open evaluation for search-on-speech based on STD in Spanish and an analysis of the results. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation consists in retrieving the speech files that contain the search terms, providing their start and end times, and a score value that reflects the confidence given to the detection. Two different Spanish speech databases have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops, and EPIC database, which comprises a set of European Parliament sessions in Spanish. We present the evaluation itself, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a detailed discussion. Five different research groups took part in the evaluation, and ten different systems were submitted in total. We compare the systems submitted to the evaluation and make a deep analysis based on some search term properties (term length, within-vocabulary/out-of-vocabulary terms, single-word/ multi-word terms, and native (Spanish)/foreign terms).

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Section: Word-based Std Systemmentioning

confidence: 99%

ALBAYZIN 2016 spoken term detection evaluation: an international open competitive evaluation in Spanish

Tejedor

Toledano

López-Otero

et al. 2017

J AUDIO SPEECH MUSIC PROC.

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“…The lattice indexing technique, described in [124], first converts the word lattices of all the utterances in the speech data from Table 9 System summary in terms of the ASR subsystem and STD subsystem employed. "prob."…”

Section: Kaldi-based Std Systemmentioning

confidence: 99%

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

Tejedor

Toledano

López-Otero

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).

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“…Lattices generated by the BMMI models are processed using the lattice indexing technique described in [18]. The lattices of all the utterances in the eval set are converted from individual finite state transducers (FST) output by Kaldi to a single generalized factor transducer structure in which the start-time, end-time and lattice posterior probability of each word token in every lattice is stored as a 3-dimensional cost associated with that instance of the word.…”

Section: Openfst-based Kws System Descriptionmentioning

confidence: 99%

“…This factor transducer is, in essence, an inverted index of all word sequences seen in the collection of eval set lattices, and permits further manipulation easily using the Google OpenFST tools [19]. Interested readers are referred to [18] for details.…”

Section: Openfst-based Kws System Descriptionmentioning

confidence: 99%

Quantifying the value of pronunciation lexicons for keyword search in lowresource languages

Chen

Khudanpur

Povey

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper quantifies the value of pronunciation lexicons in large vocabulary continuous speech recognition (LVCSR) systems that support keyword search (KWS) in low resource languages. Stateof-the-art LVCSR and KWS systems are developed for conversational telephone speech in Tagalog, and the baseline lexicon is augmented via three different grapheme-to-phoneme models that yield increasing coverage of a large Tagalog word-list. It is demonstrated that while the increased lexical coverage -or reduced out-of-vocabulary (OOV) rate -leads to only modest (ca 1%-4%) improvements in word error rate, the concomitant improvements in actual term weighted value are as much as 60%. It is also shown that incorporating the augmented lexicons into the LVCSR system before indexing speech is superior to using them post facto, e.g., for approximate phonetic matching of OOV keywords in pre-indexed lattices. These results underscore the disproportionate importance of automatic lexicon augmentation for KWS in morphologically rich languages, and advocate for using them early in the LVCSR stage.Index Terms-Speech Recognition, Keyword Search, Information Retrieval, Morphology, Speech Synthesis LOW-RESOURCE KEYWORD SEARCHThanks in part to the falling costs of storage and transmission, large volumes of speech such as oral history archives [1, 2] and on-line lectures [3,4] are now easily accessible by large user populations via the world wide web. Unlike the text-web, however, searching speech using keywords continues to be a challenging problem. Manually transcribing the speech is often prohibitively expensive. Automatic keyword search (KWS) systems are able to address the problem in some cases, but not in others, because high performance KWS systems, in turn, rely on underlying large vocabulary continuous speech recognition (LVCSR) systems that are also expensive to develop. Good LVCSR systems utilize statistical acoustic-and language-models trained from large quantities of transcribed speech and "conversational" text in the search domain, and manually crafted pronunciation lexicons with good coverage of the collection.We are interested in improving KWS performance in a low resource setting, i.e. where some resources are available to developThe authors, listed here in alphabetical order, were supported by DARPA BOLT contract Nō HR0011-12-C-0015, and IARPA BABEL contract Nō W911NF-12-C-0015. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, IARPA, DoD/ARL or the U.S. Government.an LVCSR system -such as 10 hours of transcribed speech corresponding to about 100K words of transcribed text, and a pronunciation lexicon that covers the words in the training data -but accuracy is sufficiently low that considerable improvement in K...

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Lattice Indexing for Spoken Term Detection

Cited by 128 publications

References 17 publications

ALBAYZIN 2016 spoken term detection evaluation: an international open competitive evaluation in Spanish

ALBAYZIN 2016 spoken term detection evaluation: an international open competitive evaluation in Spanish

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

Quantifying the value of pronunciation lexicons for keyword search in lowresource languages

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