This paper presents several improvements to our voice conversion system which we refer to as Speaker Transformation Algorithm using Segmental Codebooks (STASC)[2]. First, a new concept, sentence HMM, is introduced for the alignment of speech waveforms sharing the same text. This alignment technique allows reliable and high resolution mapping between two speech waveforms. In addition, it is obse...

This paper proposes a constrained structural maximum a posteriori linear regression (CSMAPLR) algorithm for further improvement of speaker adaptation performance in HMM-based speech synthesis. In the algorithm, the concept of structural maximum a posteriori (SMAP) adaptation is applied to estimation of transformation matrices of the constrained MLLR (CMLLR), where recursive MAP-based estimation...

Recently, some large-scale text dependent speaker verification systems have been tested. They show that less than 1% Equal Error Rate can be obtained on a test set score distribution. So far, the majority of impostor tests are performed using speakers who don’t really try to fool the system. This can be explained by the lack of databases recorded for this purpose, and the difficulty for a norma...

This paper presents a novel target-driven MLLR adaptation algorithm with multiply layer structure, which is based on the thorough analysis of MLLR using the generation of regression class trees. The new algorithm is constructed on the targetdriven principal. It generates the regression class dynamically, basing on the outcome of the former MLLR transformation. The regression classes is defined ...

Material and results from the current thesis may be used freely provided that the source is stated. Abstract The work presented in this report focuses on an essential problem when doing speaker adaptation; namely how eeectively the speaker speciic information in the adaptation data is used. In the project a system has been implemented for speaker adaptation of hidden Markov models (HMM's) using...

Accurate and efficient speaker canonicalization is proposed to improve the performance of speaker-independent ASR systems. Vocal tract length normalization (VTLN) is often applied to speaker canonicalization in ASR; however, it requires parallel decoding of speech when estimating the optimal warping parameter. In addition, VTLN provides the same linear spectral transformation in an utterance, a...

Speaker diarization finds contiguous speaker segments in an audio stream and clusters them by speaker identity, without using a-priori knowledge about the number of speakers or enrollment data. Diarization typically clusters speech segments based on short-term spectral features. In prior work, we showed that neural networks can serve as discriminative feature transformers for diarization by tra...

Vocal Tract Length Normalization (VLTN), usually implemented as a frequency warping procedure (e.g. bilinear transformation), has been used successfully to adapt the spectral characteristics to a target speaker in speech recognition. In this study we exploit the same concept of frequency warping but concentrate explicitly on mapping the first four formant frequencies of 5 long vowels from sourc...

This paper presents a novel target-driven MLLR adaptation algorithm with multiply layer structure, which is based on the thorough analysis of MLLR using the generation of regression class trees. The new algorithm is constructed on the targetdriven principal. It generates the regression class dynamically, basing on the outcome of the former MLLR transformation. The regression classes is defined ...