Toward Robust Speech Recognition and Understanding

Toward Robust Speech Recognition and Understanding

Toward Robust Multimodal Speech Recognition

In this paper, a robust multimodal speech recognition system is proposed in order to improve the performance of automatic speech recognition (ASR). A visual feature extraction technique for real-world data is developed and implemented. Multi-stream hidden Markov models (HMMs) including weighting factors are used to combine audio and visual information, applying the stream-weight optimization sc...

Towards robust conversational speech recognition and understanding

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

Robust methods in automatic speech recognition and understanding

This paper overviews robust architecture and modeling techniques for automatic speech recognition and understanding. The topics include robust acoustic and language modeling for spontaneous speech recognition, unsupervised adaptation of acoustic and language models, robust architecture for spoken dialogue systems, multi-modal speech recognition, and speech understanding. This paper also discuss...