A Segmental HMM for Spee h WaveformsKannan

Clustering Wide-Contexts and HMM Topologies for Spontaneous Spee h Re ognition

IDIAP Resear h Report 01 - 33 Robust HMM - Based Spee h / Musi Segmentation Jitendra Ajmera Iain

HMM composition of segmental unit input HMM for noisy speech recognition

For robust speech recognition in noisy environments, various methods have been studied. In this paper, we apply parallel model combination (PMC) for segmental unit input HMM to recognize corrupted speech in additive noise. Since several successive frames are combined and treated as an input vector in segmental unit input modeling, the increased dimension of vector degrades the precision in esti...

The Use of Recurrent Neural Networks in Continuous Speech Recognition

This chapter was written in 1994. Further advances have been made such as: context-dependent phone modelling; forward-backward training and adaptation using linear input transformations. This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is ...

Speech enhancement based on hidden Markov model using sparse code shrinkage

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...