Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speech recognition output and for aiding downstream language processing. This arti...

For many years the human auditory system has been an inspiration for developers of automatic speech recognition systems because of its ability to interpret speech accurately in a wide variety of difficult acoustical environments. This paper discusses the application of physiologically-motivated approaches to signal processing that facilitate robust automatic speech recognition in environments w...

Automatic speech recognition is being used in a variety of assistive contexts, including home computer systems, mobile telephones, and various public and private telephony services. Despite their growing presence, commercial speech recognition technologies are still not easily employed by individuals who have speech or communication disorders. While speech disorders in older adults are common, ...

Numerous examinations are performed related to automatic emotion recognition and speech detection in the Laboratory of Speech Acoustics. This article reviews results achieved for automatic emotion recognition experiments on spontaneous speech databases on the base of the acoustical information only. Different acoustic parameters were compared for the acoustical preprocessing, and Support Vector...

This paper surveys current text and speech summarization evaluation approaches. It discusses advantages and disadvantages of these, with the goal of identifying summarization techniques most suitable to speech summarization. Precision/recall schemes, as well as summary accuracy measures which incorporate weightings based on multiple human decisions, are suggested as particularly suitable in eva...

The central issues in the study of speech recognition by human listeners (HSR) and of automatic speech recognition (ASR) are clearly comparable; nevertheless the research communities that concern themselves with ASR and HSR are largely distinct. This paper compares the research objectives of the two fields, and attempts to draw informative lessons from one to the other.

This essay investigates the design of automatic speech recognition (ASR) technologies as a site at which the human qualities of ‘hearing’ and ‘understanding’ are mimicked in machines. On the basis of an observational study, it explains the major work components and debates in ASR, such as research paradigms and strategies, as well as the intricacies of instruments and experimentation. Key argum...

In previous work, we developed a closed-loop speech chain model based on deep learning, in which the architecture enabled the automatic speech recognition (ASR) and text-to-speech synthesis (TTS) components to mutually improve their performance. This was accomplished by the two parts teaching each other using both labeled and unlabeled data. This approach could significantly improve model perfo...

Speech recognition in reverberant environments is a difficult task. Reverberation has the effect of degradation of recognition performance due to acoustic mismatch. We present an optimization method of the wavelet parameters for dereverberation in automatic speech recognition (ASR). By tuning the wavelet parameters to improve the acoustic model likelihood, waveletbased dereverberation methods b...