, gaining insight into further improvements based on error analysis is very difficult. Second, LVCSR systems often take a significantly longer time to train and test new research ideas compared to small vocabulary tasks. A small vocabulary task like TIMIT provides a phonetically rich and hand-labeled corpus

The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other

comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes---providing on average a 27% reduction in error over the multi

extensively on a resource-rich language such as English. We then derive phonemic representations for any desired word in any target language, by a process of cross-language phonemic mapping. We show that this results in high accuracy recognition of vocabularies of up to several dozen words – enough for many

Document Version Accepted manuscript, peer reviewed version Link to publication from Aalborg University Citation for published version (APA):

Considerable progress has been made in the field of automatic speech recognition in recent years, especially for high-quality (full bandwidth and noise-free) speech. However, good recognition accuracy is difficult to achieve when the incoming speech is passed through a telephone channel. At the same time, the task of speech recognition over telephone lines is growing in importance, as the number of applications of spoken language processing involving telephone speech increases every day. The paper presents our recent work on developing a robust speaker-independent isolated-spoken word recognition system based on a hybrid approach (classic – artificial neural network). A number of experiments are described and compared in order to evaluate different analysis and recognition techniques that are best suited for a telephone-speech recognition task. In particular, we address the use of RASTA processing (i.e., filtering the temporal trajectories of speech parameters) for increasing the recognition accuracy. Also, we propose a method based on the adaptive filter theory for producing simulated telephone data starting from clean speech databases.

www.cstr.ed.ac.uk We present a conversational telephone speech data set designed to support research on novel acoustic models. Small vocabulary tasks from 10 words up to 500 words are defined using subsets of the Switchboard-1 corpus; each task has a completely closed vocabulary (an OOV rate of 0

ABSTRACT: In this paper we propose a new, simple approach to small vocabulary word recognition. The methodology adopted is based on hybrid fuzzy learning. In particular, the application is based on the management of an extractor hood for a cooker by means of simple vocal messages. It therefore

This paper presents results of electromyographicbased (EMG-based) speech recognition on a small vocabulary of 15 English words. The work was motivated in part by a desire to mitigate the effects of high acoustic noise on speech intelligibility in communication systems used by first responders. Both

resources available to hidden Markov model based speech recognition. Real-time performance is therefore achieved for small-vocabulary tasks, such as connected-digit recognition. In the paper, we discuss the prototype architecture based on the ViaVoice TM engine, the basic algorithms employed