Posterior Based Keyword Spotting with A Priori Thresholds

In this paper, we propose a new posterior based scoring approach for keyword and non keyword (garbage) elements. The estimation of these scores is based on HMM state posterior probability definition, taking into account long contextual information and the prior knowledge (e.g. keyword model topology). The state posteriors are then integrated into keyword and garbage posteriors for every frame. These posteriors are used to make a decision on detection of the keyword at each frame. The frame level decisions are then accumulated (in this case, by counting) to make a global decision on having the keyword in the utterance. In this way, the contribution of possible outliers are minimized, as opposed to the conventional Viterbi decoding approach which accumulates likelihoods. Experiments on keywords from the Conversational Telephone Speech (CTS) and Numbers'95 databases are reported. Results show that the new scoring approach leads to better trade off between true and false alarms compared to the Viterbi decoding approach, while also providing the possibility to precalculate keyword specific spotting thresholds related to the length of the keywords

Enhancing posterior based speech recognition systems

The use of local phoneme posterior probabilities has been increasingly explored for improving speech recognition systems. Hybrid hidden Markov model / artificial neural network (HMM/ANN) and Tandem are the most successful examples of such systems. In this thesis, we present a principled framework for enhancing the estimation of local posteriors, by integrating phonetic and lexical knowledge, as well as long contextual information. This framework allows for hierarchical estimation, integration and use of local posteriors from the phoneme up to the word level. We propose two approaches for enhancing the posteriors. In the first approach, phoneme posteriors estimated with an ANN (particularly multi-layer Perceptron – MLP) are used as emission probabilities in HMM forward-backward recursions. This yields new enhanced posterior estimates integrating HMM topological constraints (encoding specific phonetic and lexical knowledge), and long context. In the second approach, a temporal context of the regular MLP posteriors is post-processed by a secondary MLP, in order to learn inter and intra dependencies among the phoneme posteriors. The learned knowledge is integrated in the posterior estimation during the inference (forward pass) of the second MLP, resulting in enhanced posteriors. The use of resulting local enhanced posteriors is investigated in a wide range of posterior based speech recognition systems (e.g. Tandem and hybrid HMM/ANN), as a replacement or in combination with the regular MLP posteriors. The enhanced posteriors consistently outperform the regular posteriors in different applications over small and large vocabulary databases

A novel two-level architecture plus confidence measures for a keyword spotting system.

This is an electronic version of the paper presented at the V Jornadas en Tecnología del Habla, held in 2008 on BilbaoIn this work, we present a novel two-level architecture for a keyword spotting system. The first level is composed of an HMM-based keyword spotting process. The second level uses isolated word recognition. Two confidence measures in the decision stage, based on the posteriors and the keywords hypothesised by this second level, are presented and compared within the keyword spotting system. Both confidence measures outperform the performance of the first level in isolation.This work was partly funded by the Spanish Ministry of Science and Education (TIN 2005- 06885)

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model - artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm generates a new model for a keyword which takes into account the systematic errors made by the neural network and avoiding those models that can be confused with other words in the language. The new keyword model improves the keyword detection rate while minimally increasing the number of false alarms