Journal ArticleDOI
Frequency warping for VTLN and speaker adaptation by linear transformation of standard MFCC
TLDR
The performance of the new LT was comparable to that of regular VTLN implemented by warping the Mel filterbank, when the MLS criterion was used for FW estimation, and it is shown that the approximations involved do not lead to any performance degradation.About:
This article is published in Computer Speech & Language.The article was published on 2009-01-01. It has received 46 citations till now.read more
Citations
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Proceedings ArticleDOI
Accent conversion through cross-speaker articulatory synthesis
TL;DR: This work builds a cross-speaker forward mapping (CSFM) to generate L2 acoustic observations directly from L1 articulatory trajectories and evaluated the CSFM against a baseline articulatory synthesizer trained with L2 articulators.
Journal ArticleDOI
Using Phonetic Posteriorgram Based Frame Pairing for Segmental Accent Conversion
TL;DR: A new approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity, which outperforms the prior approach and can be applied to non-parallel training data, achieving the same accent conversion performance.
Patent
Method and system for cross-lingual voice conversion
TL;DR: In this article, a method and system for cross-lingual voice conversion is described, where a hidden Markov model (HMM) HMM based speech modeling for both recognizing input speech and synthesizing output speech is presented.
Journal ArticleDOI
VTLN Using Analytically Determined Linear-Transformation on Conventional MFCC
D. R. Sanand,Srinivasan Umesh +1 more
TL;DR: A method to analytically obtain a linear-transformation on the conventional Mel frequency cepstral coefficients (MFCC) features that corresponds to conventional vocal tract length normalization (VTLN)-warped MFCC features, thereby simplifying the VTLN processing.
Journal ArticleDOI
Maximum Entropy-Based Reinforcement Learning Using a Confidence Measure in Speech Recognition for Telephone Speech
TL;DR: A two-step Viterbi decoding is presented which estimates a correction factor for the observation log-likelihoods that makes the recognized and neighboring HMMs more or less likely by using a confidence score.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
S. Davis,Paul Mermelstein +1 more
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
Journal ArticleDOI
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
TL;DR: An important feature of the method is that arbitrary adaptation data can be used—no special enrolment sentences are needed and that as more data is used the adaptation performance improves.
Journal ArticleDOI
Maximum likelihood linear transformations for HMM-based speech recognition
TL;DR: The paper compares the two possible forms of model-based transforms: unconstrained, where any combination of mean and variance transform may be used, and constrained, which requires the variance transform to have the same form as the mean transform.
Journal ArticleDOI
Minimum classification error rate methods for speech recognition
TL;DR: The issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory is discussed, and the superiority of the minimum classification error (MCE) method over the distribution estimation method is shown by providing the results of several key speech recognition experiments.
Related Papers (5)
Vocal tract normalization equals linear transformation in cepstral space
Michael Pitz,Hermann Ney +1 more