Signals originated from the same speech source usually appear differently depending on a variety of acoustic effects such as the background noises, linear or nonlinear distortions incurred by the recording devices or reverberations. These acoustical effects result in mismatches between the trained speech recognition models and the input speech. One of the well-known approaches to reduce this mismatch is to map the distorted speech feature to its clean counterpart. The mapping function is usually trained based on a set of stereo data which consists of the simultaneous recordings obtained in both the reference and target conditions. In this paper, we propose the switching linear dynamic system (SLDS) as a useful model for speech feature sequence mapping. In contrast to the conventional vector-to-vector mapping algorithms, SLDS can describe sequence-to-sequence mapping in a systematic way. The proposed approach is applied to robust speech recognition in various environmental conditions and shows a dramatic improvement in recognition performance.

The performance of a speech recognition system may be degraded even without any background noise because of the linear or non-linear distortions incurred by recording devices or reverberations. One of the well-known approaches to reduce this channel distortion is feature mapping which maps the distorted speech feature to its clean counterpart. The feature mapping rule is usually trained based on a set of stereo data which consists of the simultaneous recordings obtained in both the reference and target conditions. In this paper, we propose a novel approach to speech feature sequence mapping based on the switching linear dynamic transducer (SLDT). The proposed algorithm enables us a sequence-to-sequence mapping in a systematic way, instead of the traditional vector-to-vector mapping. The proposed approach is applied to compensate channel distortion in speech recognition and shows improvement in recognition performance.

In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationmaximization (EM) algorithm. In this paper, we extend the FMLLR structure from diagonal to unrestricted full matrix with a sophisticated algorithm for the training of relevant parameters. In the experiments on artificial generation of singing voice, we evaluate the performance of the FMLLR technique with two matrix structures and also compare with other approaches to parameter adaptation in HMM-based speech synthesis. Index Terms: Parameter adaptation, MLLR, HRHSMM, factored MLLR

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this letter, we extend MLLR to factored MLLR (FMLLR) in which the MLLR parameters depend on a continuous-valued control vector. Since it is practically impossible to estimate the MLLR parameters for each control vector separately, we propose a compact parametric form of the MLLR parameters. In the proposed approach, each MLLR parameter is represented as an inner product between a regression vector and transformed control vector. We present an algorithm to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. The proposed approach is applied to adapt the HMM parameters obtained from a database of reading-style speech to singing-style voices while treating the pitches and durations extracted from the musical notes as the control vectors. This enables to efficiently construct a singing voice synthesizer with only a small amount of singing data.

It is generally known that a well-designed excitation produces high quality signals in hidden Markov model (HMM)-based speech synthesis systems. This paper proposes a novel tech- niques for generating excitation based on the waveform inter- polation (WI). For modeling WI parameters, we implemented statistical method like principal component analysis (PCA). The parameters of the proposed excitation modeling techniques can be easily combined with the conventional speech synthesis sys- tem under the HMM framework. From a number of experi- ments, the proposed method has been found to generate more naturally sounding speech. Index Terms: HMM-based speech synthesis, Waveform Inter- polation, Principal Component Analysis In this paper, we propose a novel approach to excitation modeling under the waveform interpolation (WI) framework. For parameterizing the excitation generation model, a charac- teristic waveform (CW) is extracted from each frame of LP residual signals. To derive a compact representation of each CW, we apply principal component analysis (PCA) to a collec- tion of the extracted CW's. Once PCA is done, each CW can be compactly approximated as a linear combination of a few PCA basis vectors. The statistical distribution of the linear com- bination coefficients and their dynamics can be efficiently de- scribed by means of HMM's for which the relevant parameters are estimated by following the conventional HMM training pro- cedure. Given a sentence we want to synthesize, the sequence of CW's can be generated from the trained HMM's according to the maximum likelihood (ML) criterion. The WI algorithm enables a smooth transition between adjacent CW's resulting in a more natural excitation signal. The major advantages of the proposed technique are twofold. First, instead of using a fixed set of waveforms such as the impulse train and the ran- dom noise, the proposed method finds CWs which represents the excitation waveforms from the various kinds of modeling in frequency domain. Second, the WI approach lets the excita- tion signal evolve smoothly, which may reduce the audible arti- facts of the synthesized speech. From a number of experiments on speech synthesis, it has been demonstrated that the propose technique enhances the quality of the synthesized speech.