Score normalization and system combination for improved keyword spotting

Karakos, Damianos; Schwartz, Richard; Tsakalidis, Stavros; Zhang, Le; Ranjan, Shivesh; Ng, Tim; Hsiao, Roger; Saikumar, Guruprasad; Bulyko, Ivan; Nguyen, Long; Makhoul, John; Grézl, František; Hannemann, Mirko; Karafiát, Martin; Szöke, Igor; Veselý, Karel; Lamel, Lori; Le, Viet-Bac

doi:10.1109/asru.2013.6707731

Cited by 77 publications

(57 citation statements)

References 11 publications

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“…This reduces the variability in scores across different queries and make them comparable for final evaluation. We have also tried sumto-1 (STO) normalization and keyword-specific thresholds (KST) [49], [50]. However they did not perform better than the mean-variance normalization.…”

Section: Feature Extraction and Pre-processingmentioning

confidence: 99%

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Ram

Asaei

Bourlard

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on dynamic programming based template matching techniques using phone posterior features extracted at the output of a deep neural network (DNN). Previously, it has been shown that the space of phone posteriors is highly structured, as a union of low-dimensional subspaces. To exploit the temporal and sparse structure of the speech data, we investigate here three different QbE-STD systems based on sparse model recovery. More specifically, we use query examples to model the query subspace using dictionary for sparse coding. Reconstruction errors calculated using sparse representation of feature vectors are then used to characterize the underlying subspaces. The first approach uses these reconstruction errors in a dynamic programming framework to detect the spoken query, resulting in a much faster search compared to standard template matching. The other two methods aim at merging template matching and sparsity based approaches to further improve the performance. The first one proposes to regularize the template matching local distances using sparse reconstruction errors. The second approach aims at using the sparse reconstruction errors to rescore (improve) the template matching likelihood. Experiments on two different databases (AMI and MediaEval) show that the proposed hybrid systems perform better than a highly competitive QbE-STD baseline system.

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Section: Feature Extraction and Pre-processingmentioning

confidence: 99%

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Ram

Asaei

Bourlard

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The keyword scores are then normalized and calibrated using the BBN KST normalization tool [12]. Decision about keeping or ignoring the keyword hits is based on a defined threshold (0.5 in our experiments).…”

Section: System Descriptionmentioning

confidence: 99%

Improving data selection for low-resource STT and KWS

Fraga-Silva

Laurent

Gauvain

et al. 2015

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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This paper extends recent research on training data selection for speech transcription and keyword spotting system development. Selection techniques were explored in the context of the IARPA-Babel Active Learning (AL) task for 6 languages. Different selection criteria were considered with the goal of improving over a system built using a pre-defined 3-hour training data set. Four variants of the entropy-based criterion were explored: words, triphones, phones as well as the use of HMM-states previously introduced in [4]. The influence of the number of HMM-states was assessed as well as whether automatic or manual reference transcripts were used. The combination of selection criteria was investigated, and a novel multi-stage selection method proposed. This method was also assessed using larger data sets than were permitted in the Babel AL task. Results are reported for the 6 languages. The multi-stage selection was also applied to the surprise language (Swahili) in the NIST OpenKWS 2015 evaluation.

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“…For the two measures, score normalization [2,7,8] has been proved to be essential. Keyword specific threshold (KST) normalization [9] and sum-to-one (STO) normalization [2] are the two mainstream score normalization methods.…”

Section: Introductionmentioning

confidence: 99%

Improved system fusion for keyword search

Cai

Lü

et al. 2015

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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It has been demonstrated that system fusion can significantly improve the performance of keyword search. In this paper, we compare the performance of several widely-used arithmeticbased fusion methods using different normalization pipeline and try to find the best pipeline. A novel arithmetic-based fusion method is proposed in this work. The method supplies a more effective way to incorporate the number of systems which have non-zero scores for a detection. When tested on the development test dataset of the OpenKWS15 Evaluation, the proposed method achieves the highest maximum termweighted value (MTWV) and actual term-weighted value (ATWV) among all other arithmetic-based fusion methods. Usually, discriminative fusion methods employing classifiers can outperform arithmetic-based fusion methods. A DNNbased fusion method is explored in this work. After wordburst information is added, the DNN-based fusion method outperforms all other methods. In addition, it is notable that our arithmetic-based method achieves the same MTWV as the DNN-based method.

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Score normalization and system combination for improved keyword spotting

Cited by 77 publications

References 11 publications

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Improving data selection for low-resource STT and KWS

Improved system fusion for keyword search

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