in a voice conversion system speech signal of a speaker (i.e. source speaker) is modified so that it sounds as if it had been pronounced by b speaker (i.e. target speaker). this process, sometimes, is called speaker conversion (changing speaker identity). achieved signal from speaker conversion system is desired to have high quality and very natural. to satisfy this, three major methods are pro...

This paper proposes a new method for text-dependent speaker recognition. The scheme is based on learning (what we refer to as) speaker-specific compensators for each speaker in the system. The compensator is essentially a speaker to speaker transformation which enables the recognition of the speech of one speaker through a speaker-dependent speech recognition system built for the other. Such a ...

Absract--Voice conversion involves transformation of speaker characteristics in a speech uttered by a speaker called source speaker to generate a speech having voice characteristics of a desired speaker called the target speaker. Voice conversion is used in many applications namely dubbing, to enhance the quality of the speech, text-to-speech synthesizers, online games, multimedia, music, cross...

We propose a signal processing method that transforms foreign-accented speech to resemble its native-accented counterpart. The problem is closely related to voice conversion, except that our method seeks to preserve the organic properties of the foreign speaker’s voice; i.e., only those features which cue foreign-accentedness are to be transformed. Our method operates at two levels: prosodic an...

Adaptation of speaker-independent hidden Markov models (HMM’s) to a new speaker using speaker-specific data is an effective approach to reinforce speech recognition performance for the enrolled speaker. Practically, it is desirable to flexibly perform the adaptation without any knowledge or limitation on the enrolled adaptation data (e.g. data transcription, length and content). However, the in...

Adaptation of speaker-independent hidden Markov models (HMM’s) to a new speaker using speaker-specific data is an effective approach to reinforce speech recognition performance for the enrolled speaker. Practically, it is desirable to flexibly perform the adaptation without any knowledge or limitation on the enrolled adaptation data (e.g. data transcription, length and content). However, the in...

This paper presents a novel framework of on-line hierarchical transformation of hidden Markov models (HMM’s) for speaker adaptation. Our aim is to incrementally transform (or adapt) all the HMM parameters to a new speaker even though part of HMM units are unseen in adaptation data. The transformation paradigm is formulated according to the approximate Bayesian estimate, which the prior statisti...