Feature analysis for discriminative confidence estimation in spoken term detection

Abstract

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractDiscriminative confidence based on multi-layer perceptrons (MLPs) and multiple features has shown significant advantage compared to the widely used lattice-based confidence in spoken term detection (STD). Although the MLP-based framework can handle any features derived from a multitude of sources, choosing all possible features may lead to over complex models and hence less generality. In this paper, we design an extensive set of features and analyze their contribution to STD individually and as a group. The main goal is to choose a small set of features that are sufficiently informative while keeping the model simple and generalizable. We employ two established models to conduct the analysis: one is linear regression which targets for the most relevant features and the other is logistic linear regression which targets for the most discriminative features. We find the most informative features are comprised of those derived from diverse sources (ASR decoding, duration and lexical properties) and the two models deliver highly consistent feature ranks. STD experiments on both English and Spanish data demonstrate significant performance gains with the proposed feature sets.

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

“…Spanish is a major language in the world and significant research has been conducted on it for ASR, KeyWord Spotting (KWS), and STD tasks [64][65][66][67][68][69][70]. The increasing interest in search-on-speech around the world and the lack of search-on-speech evaluations dealing with Spanish language encouraged us to organize a series of STD evaluations starting in 2012 and held biannually until 2016 aiming to evaluate the progress in this technology for Spanish.…”

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

“…The Spanish language is widespread throughout the world, and significant research has been conducted into it for ASR [58][59][60], KWS [61,62], and STD [62][63][64]. This, combined with the success of the ALBAYZIN QbE STD evaluations held in 2012 and 2014, have encouraged us to organize a new QbE STD evaluation for the 2016 ALBAYZIN evaluation campaign which aims to evaluate the progress in this technology in Spanish.…”

Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations

“…Spanish is a major language in the world and significant research has been conducted on it for ASR, KWS, and STD tasks [46][47][48][49][50][51][52]. In 2012, the first QbE STD evaluation dealing with Spanish was organized in the context of the ALBAYZIN 2012 Evaluation campaign.…”