The lack of noise robustness is one of the main drawbacks of an Automatic Speech Recognition (ASR) system. A well trained ASR system can achieve high recognition rate on quiet laboratory conditions, but perform poorly in real life environments. In this paper we will present a noise robustness method which uses the clean speech Hidden Markov Models (HMM) and noise statistics, to create an approx...

During spoken-word recognition, listeners experience phonological competition between multiple word candidates, which increases, relative to optimal listening conditions, when speech is masked by noise. Moreover, listeners activate semantic word knowledge during the word’s unfolding. Here, we replicated the effect of background noise on phonological competition and investigated to which extent ...

In this work, new multi-classifier schemes for isolated word speech recognition based on the combination of standard Hidden Markov Models (HMMs) and Complementary Gaussian Mixture Models (CGMMs) are proposed. Typically, in speech recognition systems, each word or phoneme in the vocabulary is represented by a model trained with samples of each particular class. The recognition is then performed ...

We present a new system for the recognition of cursive handwriting that is based on a perceptive model and neural networks. At the high level, our system takes into account several psychological effects such as the word superiority effect. At the low level, it utilizes a global feature extraction method which models how some features might be preattentively detected by the human visual system. ...

Auditory word recognition in the non-dominant language has been suggested to break down under noisy conditions due, part, difficulty of deriving a benefit from contextually constraining information. However, previous studies examining effects sentence constraints on noise have conflated multiple psycholinguistic processes umbrella term “predictability”. The present study improves these by narro...

We present a probabilistic framework that uses a bone sensor and air microphone to perform speech enhancement for robust speech recognition. The system exploits advantages of both sensors: the noise resistance of the bone sensor, and the linearity of the air microphone. In this paper we describe the general properties of the bone sensor relative to conventional air sensors. We propose a model c...

This paper proposes a noise robust speech recognition method for Japanese utterances using prosodic information. In Japanese, the fundamental frequency (F0) contour conveys phrase intonation and word accent information. Consequently, it also conveys information about prosodic phrase and word boundaries. This paper first proposes a noise robust F0 extraction method using the Hough transform, whi...

A human-symbiotic robot called “EMIEW2” and its auditory function which includes two noise reduction methods against self-generated mechanical noise and external floor-level noise is introduced. The former type of noise is produced by the robot itself, and this is a difficult problem because it can be loud, nonstationary, and have a wide frequency band. We adopt a maximized SNR technique, in wh...