Automatic Speech Recognition from Neural Signals: A Focused Review

Automatic Speech Recognition from Neural Signals: A Focused Review

Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-...

Improving automatic emotion recognition from speech signals

We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the ev...

Automatic Speech Recognition System: A Review

Speech is the most prominent & primary mode of Communication among human beings. Now-a-days Speech also has potential of being important mode of interaction with computers. This paper gives an overview of Automatic Speech Recognition System, Classification of Speech Recognition System and also includes overview of the steps followed for developing the Speech Recognition System in stages. This p...

Automatic speech recognition with neural spike trains

A major difference between the human auditory system and automatic speech recognition (ASR) lies in their representation of sound signals: whereas ASR uses a smoothed low-dimensional temporal and spectral representation of sound signals, our hearing system relies on extremely high-dimensional but temporally sparse spike trains. A strength of the latter representation is in the inherent coding o...

Neural Networks in Automatic Speech Recognition