Emergency dispatching based on automatic speech recognition

Automatic Speech Recognition Based on Electromyographic Biosignals

This paper presents our studies of automatic speech recognition based on electromyographic biosignals captured from the articulatory muscles in the face using surface electrodes. We develop a phone-based speech recognizer and describe how the performance of this recognizer improves by carefully designing and tailoring the extraction of relevant speech feature toward electromyographic signals. O...

Automatic Speech Recognition Based on Neural Networks

Auditory-based automatic speech recognition

In this paper we develop a physiologically motivated model of peripheral auditory processing and evaluate how the different processing steps influence automatic speech recognition in noise. The model features large dynamic compression (>60 dB) and a realistic sensory cell model. The compression range was well matched to the limited dynamic range of the sensory cells and the model yielded surpri...

Syllable-based Automatic Arabic Speech Recognition

In this paper, we concentrate on the automatic recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consi...

Web-Based Automatic Speech Recognition Service - webASR

A state-of-the-art automatic speech recognition (ASR) system was developed as part of the AMIDA project whose core domain was the transcription of small to medium sized meetings. The system has performed well in recent NIST evaluations (RT’07 and RT’09). This research-grade ASR system has now been made available as a free web service (webASR) targeting non-commercial researchers. Access to the ...