Mismatches between application and training data greatly reduce the performance of automatic speech recognition (ASR) systems. However, collecting suitable amounts of in-domain and application-specific data for training is resource intensive and may not be feasible for resource-scarce environments. Utilising limited amounts of in-domain data and a combination of feature normalisation and acoust...

A major challenge for automatic speech recognition (ASR) relates to significant performance reduction in noisy environments. Recent research has shown that auditory features based on Gammatone filters are promising to improve robustness of ASR systems against noise, though the research is far from extensive and generalizability of the new features is unknown. This paper presents our implementat...

Speech corpus is the backbone of an Automatic speech Recognition system. This paper presents the development of speech corpora for different speech recognition tasks in Malayalam language. Pronunciation dictionary and Transcription file which are the other two essential resources for building a speech recognizer are also being created. Speech recognition performance of different speech recognit...

For about four decades human beings have been dreaming of an intelligent machine which can master the natural speech. In its simplest form, this machine should consist of two subsystems, namely automatic speech recognition (ASR) and speech understanding (SU). The goal of ASR is to transcribe natural speech while SU is to understand the meaning of the transcription. Recognizing and understanding...

Speech-to-text summarization systems usually take as input the output of an automatic speech recognition (ASR) system that is affected by issues like speech recognition errors, disfluencies, or difficulties in the accurate identification of sentence boundaries. We propose the inclusion of related, solid background information to cope with the difficulties of summarizing spoken language and the ...

We have proposed an automatic speech summarization approach that extracts words from transcription results obtained by automatic speech recognition (ASR) systems. To numerically evaluate this approach, the automatic summarization results are compared with manual summarization generated by human subjects through word extraction. We have proposed three metrics, weighted word precision, word strin...

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 study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. In this paper we present a study performed to analyze different machine learning techniques validity in automatic speech emotion recognition area. Using a bilingu...

Automatic speech recognition, translating of spoken words into text, is still a challenging task due to the high viability in speech signals. Deep learning, sometimes referred as representation learning or unsupervised feature learning, is a new area of machine learning. Deep learning is becoming a mainstream technology for speech recognition and has successfully replaced Gaussian mixtures for ...