This paper describes a classification technique for emotional expressions and speaking styles of speech using only a small amount of training data of a target speaker. We model spectral and fundamental frequency (F0) features simultaneously using multi-space probability distribution HMM (MSD-HMM), and adapt a speaker-independent neutral style model to a certain target speaker’s style model with...

The uncertainty in parameter estimation due to the adverse environments deteriorates the speech recognition performance. It becomes crucial to incorporate the parameter uncertainty into decision so that the classification robustness can be assured. In this paper, we propose a linear regression based Bayesian predictive classification (LRBPC) for robust speech recognition. This framework is cons...

In this paper, we propose a novel algorithm for rapid speaker adaptation based on our Structural Maximum Likelihood Eigenspace Mapping (SMLEM). The proposed method constructs a binary-tree structured hierarchical Speaker Independent (SI) eigenspace at different levels from well-trained SI system models, and then dynamically constructs a new set of speaker dependent (SD) eigenspaces at correspon...

This paper addresses the use of discriminative training criteria for Speaker Adaptive Training (SAT), where both the transform generation and model parameter estimation are estimated using the Minimum Phone Error (MPE) criterion. In a similar fashion to the use of I-smoothing for standard MPE training, a smoothing technique is introduced to avoid over-training when optimizing MPEbased feature-s...

A new adaptation strategy for distant noisy speech is created by phoneme class based approaches for context-independent acoustic models. Unlike the previous approaches such as MLLR-MAP adaptation which adapts acoustic model to the features, our phoneme-class based adaptation (PCBA) adapts the distant data features to our acoustic model which has trained on close microphone TIMIT sentences. The ...

In this paper the Philips Broadcast News transcription system is described. The Broadcast News task aims at the recognition of \found" speech in radio and television broadcasts without any additional side information (e.g. speaking style, background conditions). The system was derived from the Philips continuous mixture density crossword HMM system, using MFCC features and Laplacian densities. ...

We investigate a variety of methods for improving language recognition accuracy based on techniques in speech recognition, and in some cases borrowed from speaker recognition. First, we look at the question of language-dependent versus language-independent phone recognition for phonotactic (PRLM) language recognizers, and find that language-independent recognizers give superior performance in b...

Speaker clustering is one of the major methods for speaker adaptation. MLLR (Maximum Likelihood Linear Regression) adaptation using transformation matrices corresponding to phone classes/clusters is another useful method especially when the length of utterances for adaptation is limited. In these methods, how to decide the most appropriate number of clusters is an important research issue. This...

In this paper, the theoretical framework of maximum a posteriori linear regression (MAPLR) based variance adaptation for continuous density HMMs is described. In our approach, a class of informative prior distribution for MAPLR based variance adaptation is identified, from which the close form solution of MAPLR based variance adaptation is obtained under its EM formulation. Effects of the propo...

Most conventional techniques for noise adaptation assume a clean initial speech model which is adapted to a specific noise condition using adaptation data accumulated from the condition. In this paper, a different problem is considered, i.e. adapting a noisy speech model to a specific noise condition. For example, the initial noisy model may be a multi-condition model which is used to provide m...