A hierarchical evaluation methodology in speech recognition

In speech recognition vast hypothesis spaces are generated, so the search methods used and their speedup techniques are both of great importance. One way of getting a speedup gain is to search in multiple steps. In this multipass search technique the first steps use only a rough estimate, while the latter steps apply the results of the previous ones. To construct these raw tests we use simplified phoneme groups which are based on some distance function defined over phonemes. The tests we performed show that this technique could significantly speed up the recognition process

Telephone speech recognition via the combination of knowledge sources in a segmental speech model

The currently dominant speech recognition methodology, Hidden Markov Modeling, treats speech as a stochastic random process with very simple mathematical properties. The simplistic assumptions of the model, and especially that of the independence of the observation vectors have been criticized by many in the literature, and alternative solutions have been proposed. One such alternative is segmental modeling, and the OASIS recognizer we have been working on in the recent years belongs to this category. In this paper we go one step further and suggest that we should consider speech recognition as a knowledge source combination problem. We offer a generalized algorithmic framework for this approach and show that both hidden Markov and segmental modeling are a special case of this decoding scheme. In the second part of the paper we describe the current components of the OASIS system and evaluate its performance on a very difficult recognition task, the phonetically balanced sentences of the MTBA Hungarian Telephone Speech Database. Our results show that OASIS outperforms a traditional HMM system in phoneme classification and achieves practically the same recognition scores at the sentence level

Improving the Multi-Stack Decoding Algorithm in a Segment-based Speech Recognizer

Abstract. During automatic speech recognition selecting the best hypothesis over a combinatorially huge hypothesis space is a very hard task, so selecting fast and efficient heuristics is a reasonable strategy. In this paper a general purpose heuristic, the multi-stack decoding method, was refined in several ways. For comparison, these improved methods were tested along with the well-known Viterbi beam search algorithm on a Hungarian number recognition task where the aim was to minimize the scanned hypothesis elements during the search process. The test showed that our method runs 6 times faster than the basic multi-stack decoding method, and 9 times faster than the Viterbi beam search method