This paper presents a new voice conversion algorithm which modiies the utterance of a source speaker to sound like speech from a target speaker. We refer to the method as Speaker Transformation Algorithm using Segmental Codebooks (STASC). A novel method is proposed which nds accurate alignments between source and target speaker utterances. Using the alignments, source speaker acoustic character...

This paper presents a new voice conversion algorithm which modi®es the utterance of a source speaker to sound-like speech from a target speaker. We refer to the method as Speaker Transformation Algorithm using Segmental Codebooks (STASC). A novel method is proposed which ®nds accurate alignments between source and target speaker utterances. Using the alignments, source speaker acoustic characte...

Voice transformation (also known as voice conversion or voice morphing) is a name given to techniques which take speech from one speaker as input and attempt to produce speech that sounds like it came from another speaker. One compelling argument for good voice transformation is that it reduces the difficulty in creating additional synthetic voices with new identities and styles once an existin...

This paper presents a cluster-based feature transformation technique for telephone-based speaker verification when labels of the handset types are not available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to ide...

Speaker transformation is a technique that modifies a source speaker’s speech to be perceived as if a target speaker has spoken it. Compared to statistical techniques, warping function based transformation techniques require less training data and time. The objective of this paper is to investigate the transformation using quadratic surface interpolation. Source and target utterances were analy...

This paper presents a 3-stage adaptation framework based on speaker selection training. First a subset of cohort speakers is selected for test speaker using Gaussian mixture model, which is more reliable given very limited adaptation data. Then cohort models are linearly transformed closer to each test speaker. Finally the adapted model for the test speaker is obtained by combining these transf...

In this paper, we presented an integrated on-line learning scheme, which combined the state-of-art speaker normalization and adaptation techniques to improve the performance of our large vocabulary Chinese continuous speech recognition (CSR) system. We used VTLN to remove inter-speaker variation in both training and testing stage. To facilitate dynamic transformation scale determination, we dev...

This paper presents a 3-stage adaptation framework based on speaker selection training. First a subset of cohort speakers is selected for test speaker using Gaussian mixture model, which is more reliable given very limited adaptation data. Then cohort models are linearly transformed closer to each test speaker. Finally the adapted model for the test speaker is obtained by combining these transf...

This paper evaluates the recognition performance of a system using acoustic models transformed across language boundaries. Parameters of hidden Markov models (HMMs) trained on speaker independent English data are adapted using Afrikaans adaptation data to realise speaker dependent, multispeaker and speaker independent Afrikaans models. Adaptation is performed using maximum a posteriori probabil...

Semi-supervised speaker clustering refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. In the form of an independent training set, the prior knowledge helps us learn a speaker-discriminative feature transformation, a universal speaker prior model, and a discriminative speaker subspace, or equivalently a speaker-discriminative di...