Multiple-model based speech recognition (MMSR) has been shown to be quite successful in noisy speech recognition. Since it employs multiple hidden Markov model (HMM) sets that correspond to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can be closely matched with the test noisy speech, which leads to improved performance when compared with other state-o...

Listening in noise is often perceived to be effortful. This is partly because cognitive resources are engaged in separating the target signal from background noise, leaving fewer resources for storage and processing of the content of the message in working memory. The Auditory Inference Span Test (AIST) is designed to assess listening effort by measuring the ability to maintain and process hear...

Purpose: The aim of this study was to examine test-retest reliability and validity the adaptive Korean matrix sentence-in-noise test for elderly group with normal hearing (ENH) impairment (EHI).Methods: Twenty ENH listeners 20 EHI based on World Health Organization (WHO)-proposed hearing-impairment grade participated. For test, each subject assessed twice in a 2-week interval using three types ...

The robust speech recognition system available for all environments has become a hotspot in speech research. The environment adaptive methods play an important part in improving the system robustness including PMC. In this paper, PMC is investigated deeply and further developed to achieve the better performance. In general noisy environments, the channel distortion and noise corruption are both...

We propose a new approach for detecting topic boundaries and keywords in arbitrary speech, with neither recognition nor prosodic processing, aiming at quick access to the content of recorded raw speech. This approach is based on the general tendency that frequently-repeated phrases/words in speech are characteristic of topics in discourse, so it uses pairs of phonetically similar segments (PPSS...

Speech captured from an in-ear microphone (IEM) under an intra-aural device is beneficial in extremely noisy environments as it maintains a relatively high signal to noise ratio. Due to its limited bandwidth, speech enhancement is required in order to obtain a more natural speech. Consequently, quick and practical measurement of speech quality is important. In this paper, we compare the perform...

Problem statement: In speech communication, speech coding aims at preserving the speech quality with lower coding bitrate. When considering the communication environment, various types of noises deteriorates the speech quality. The expressive speech with different speaking styles may cause different speech quality with the same coding method. Approach: This research proposed a study of speech c...

While there have been numerous studies in the field of speech enhancement, the majority of these studies have focused on noise reduction for normal-hearing (NH) individuals. In addition, no speech enhancement algorithms reported in the signal processing community have reported an improvement in intelligibility, with the exception of a recent study by Tsoukalas et al. [IEEE Transactions of Speec...

Ambient or environmental noise is a major factor that affects the performance of an automatic speech recogniser. Large vocabulary, speaker-dependent, continuous speech recognisers are commercially available. Speech recognisers perform well in a quiet environment, but poorly in a noisy environment. Speaker-dependent speech recognisers require training prior to them being tested, where the level ...