Low-resource keyword search strategies for tamil

Chen, Nancy F.; Ni, Chuanfa; Chen, I-Fan; Sivadas, Sunil; Pham, Van Tung; Xu, Haihua; Xiao, Xiong; Lau, Tze Siong; Leow, Su Jun; Lim, Boon Pang; Leung, Cheung-Chi; Wang, Lei; Lee, Chin‐Hui; Goh, Alvina; Chng, Eng Siong; Ma, Bin; Li, Haizhou

doi:10.1109/icassp.2015.7178996

Cited by 32 publications

(14 citation statements)

References 24 publications

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“…We built a morpheme-based KWS system since morpheme was shown to be effective for detecting OOV keywords [5,27,28]. We adopted the Morfessor toolkit [29] to segment both wordbased dictionary and word transcriptions into morpheme units.…”

Section: Abstractearch Systemmentioning

confidence: 99%

“…This work focuses on the Spoken Term Detection (STD) [1] or Keyword Search (KWS) [2] task, which detects the present of a textual keyword in an audio corpus. Generally, a two-stage approach is utilized for a KWS system [3][4][5][6]. Specifically, audio files of the corpus are first segmented and transcribed into lattices by an automatic speech recognition (ASR).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Abstractearch Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pham

Xiao

et al. 2016

Interspeech 2016

Self Cite

View full text Add to dashboard Cite

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“…Best performance in the NIST Open KWS 2013 evaluation is ATWV=0.6248 [110] under the Full Language Pack (FullLP) condition, for which 20 h of word-transcribed scripted speech, 80 h of word-transcribed CTS, and a pronunciation lexicon were given to participants. In the works describing systems on the surprise language (i.e., Tamil) of the Open KWS 2014 evaluation [53,92,94,[111][112][113][114][115][116][117], ATWV=0.5802 is the best performance obtained under the FullLP condition, for which 60 h of transcribed speech and a pronunciation lexicon were given to participants.…”

Section: Comparison To Other Evaluationsmentioning

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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“…Other subword units, namely morphemes, are effective for morphological rich languages such as Turkish or Tamil [122]. Morphemes are smallest grammatical units, i.e.…”

Section: Data-driven Subword Unitsmentioning

confidence: 99%

“…Various types of subwords have been studied, including linguistic units such as phones [57,58,60], syllables [62,63,138], morphemes [65,122], characters [62]; and data driven units such as word-fragments [117], multigrams [94], graphones [107] and particles [101].…”

Section: Introductionmentioning

confidence: 99%

Robust spoken term detection using partial search and re-scoring hypothesized detections techniques

Pham¹

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Low-resource keyword search strategies for tamil

Cited by 32 publications

References 24 publications

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

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

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

Robust spoken term detection using partial search and re-scoring hypothesized detections techniques

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