A major challenge faced by a spoken term detection (STD) system is the detection of out-of-vocabulary (OOV) terms. Although a subword-based STD system is able to detect OOV terms, performance reduction is always observed compared to in-vocabulary terms. One challenge that OOV terms bring to STD is the pronunciation uncertainty. A commonly used approach to address this problem is a soft matching procedure, and the other is the stochastic pronunciation modelling (SPM) proposed by the authors. In this paper we compare these two approaches, and combine them using a discriminative decision strategy. Experimental results demonstrated that SPM and soft match are highly complementary, and their combination gives significant performance improvement to OOV term detection. Index Terms — stochastic pronunciation modelling, soft match, confidence estimation, spoken term detection, speech recognition 1.

Spoken term detection (STD) is a fundamental task in spoken information retrieval. Compared to conventional speech transcription and keyword spotting, STD is an open-vocabulary task and is necessarily required to address out-of-vocabulary (OOV) terms. Approaches based on subword units, e.g. phonemes, are widely used to solve the OOV issue; however, performance on OOV terms is still significantly inferior to that for in-vocabulary (INV) terms. The performance degradation on OOV terms can be attributed to a multitude of factors. A particular factor we address in this paper is that the acoustic and language models used for speech transcribing are highly vulnerable to OOV terms, which leads to unreliable confidence measures and error-prone detections. A direct posterior confidence measure that is derived from discriminative models has been proposed for STD. In this paper, we utilize this technique to tackle the weakness of OOV terms in confidence estimation. Neither acoustic models nor language models being included in the computation, the new confidence avoids the weak modeling problem with OOV terms. Our experiments, set up on multi-party meeting speech which is highly spontaneous and conversational, demonstrate that the proposed technique improves STD performance on OOV terms significantly; when combined with conventional lattice-based confidence, a significant improvement in performance is obtained on both INVs and OOVs. Furthermore, the new confidence measure technique can be combined together with other advanced techniques for OOV treatment, such as stochastic pronunciation modeling and term-dependent confidence discrimination, which leads to an integrated solution for OOV STD with greatly improved performance.

Spoken term detection (STD) systems usually arrive at many overlapping detections which are often addressed with some pragmatic approaches, e.g. choosing the best detection to represent all the overlaps. In this paper we present a theoretical study based on a concept of acceptance space. In particular, we present two confidence estimation approaches based on Bayesian and evidence perspectives respectively. Analysis shows that both approaches possess respective advantages and shortcomings, and that their combination has the potential to provide an improved confidence estimation. Experiments conducted on meeting data confirm our analysis and show considerable performance improvement with the combined approach, in particular for out-of-vocabulary spoken term detection with stochastic pronunciation modeling. Index Terms — Confidence measurement, stochastic pronunciation modeling, spoken term detection, speech recognition 1.