To achieve natural high quality synthesized speech in HMM-based speech synthesis, the effective modelling of complex acoustic and linguistic contexts is critical. Traditional approaches use context-dependent HMMs with decision tree based parameter clustering to model the full combinatorial of contexts. However, weak contexts, such as word-level emphasis in natural speech, are difficult to captu...

We propose novel speaker independent (SI) modeling and speaker adaptation based on a linear transformation. An SI model and speaker dependent (SD) models are usually generated using the same preprocessing of acoustic data. This straightforward preprocessing causes a serious problem. Probability distributions of the SI models become broad and the SI models do not give good initial estimates for ...

Automatic speech recognition (ASR) for dysarthric speakers is one of the most challenging research areas. The lack of corpus for dysarthric speakers makes it even more difficult. The speaker adaptation (SA) is an alternative solution to overcome the lack of dysarthric speech and enhance the performance of ASR. This paper introduces the Severity-based adaptation, using small amount of speech dat...

In this paper, we propose a speech recognition method under non-stationary musical environments using Kalman ltering speech signal estimation method and iterative unsupervised MLLR(Maximum Likelihood Linear Regression) adaptation. Our proposing method estimates the speech signal under non-stationary noisy environments such a s m usical background by applying speech state transition model to Kal...

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

In this paper, we propose an anchor modeling scheme where instead of conventional “anchor” speakers, we use eigenvectors that span the Eigen-voice space. The computational advantage of conventional Anchor-modeling based speaker identification system comes from representing all speakers in a space spanned by a small number of anchor speakers instead of having separate speaker models. The convent...

We propose a discriminative fuzzy clustering maximum a posterior linear regression (DFCMAPLR) model adaptation approach to compensate the acoustic mismatch due to speaker variability. The DFCMAPLR approach adopts the MAP criterion and a discriminative objective function to estimate shared affine transform and fuzzy weight sets, respectively. Then, through a linear combination of the calculated ...

In this paper, we propose to combine the rapid adaptation capability of conventional Vocal Tract Length Normalization (VTLN) with the computational efficiency of transform-based adaptation such as MLLR or CMLLR. VTLN requires the estimation of only one parameter and is, therefore, most suited for the cases where there is little adaptation data (i.e. rapid adaptation). In contrast, transform-bas...

Development of anti-cancer drugs towards clinical application is costly and inefficient. Large screens of drugs, efficacious for non-cancer disease, are currently being used to identify candidates for repurposing based on their anti-cancer properties. Here, we show that low-dose salinomycin, a coccidiostat ionophore previously identified in a breast cancer screen, has anti-leukemic efficacy. AM...

This paper describes a novel method for reducing the transcription effort in the construction of task-adapted acoustic models for a practical automatic speech recognition (ASR) system. We have to prepare actual data samples collected in the practical system and transcribe them for training the task-adapted acoustic models. However, transcribing utterances is a time-consuming and laborious proce...