This paper proposes a method to enhance the spectral clarity of hidden Markov model (HMM)-based text-to-speech (TTS) systems. A simple way of enhancing spectral clarity is increasing the order of spectral parameters in the speech analysis/synthesis stage, but the method has an inherent statistical modeling problem. The proposed algorithm takes a low-to-high-order spectral parameter mapping approach that adopts low-order parameters for HMM training but does high-order parameters for the actual synthesis step. Various ways of mapping criterion to find appropriate high-order parameters are investigated to further enhance the quality of synthesized speech. Performance evaluation results verify the superiority of the proposed method compared to the conventional one.

This letter proposes an HMM-based Text-to-Speech (TTS) system using waveform interpolation (WI)-based speech analysis and synthesis. The synthesized speech quality of the proposed system is significantly improved due to adopting an enhanced excitation modeling technique. The decomposition of characteristic waveform (CW) into slowly evolving waveform (SEW) and rapidly evolving waveform (REW) is efficient not only for excitation modeling but also for training process of HMMs. Objective and subjective test results verify the superiority of the proposed approach to conventional ones.

This paper presents an enhanced nuisance attribute projection (NAP) method to improve the performance of speaker verification systems in mismatched train and test conditions. Unlike the conventional NAP training method that does not take any scheme to discriminate the source of nuisance, the proposed method quantitatively estimates the source of nuisance based on the statistics of given background speakers' eigenvalues. The estimated values are used for defining a discriminative weight for each of background speakers and selectively including the statistics of between-class scatter or of within-class scatter from them. Through the scheme, we intend to design a more robust projection matrix which involves less speaker-dependent or speaker-intrinsic variability while including more latent nuisance factors beyond the common within-class scatter of backgrounds. Experimental results on the recent NIST SRE evaluations demonstrate that the proposed algorithms produce consistent improvement over the previous NAP approaches.

Automatic recognition of paralinguistic information from speech is important. Speaker identity, gender, age range, emotional state, etc. Guide human computer interaction systems to automatically adapt to different user needs.

In this paper, an information theoretic approach to selecting feature frames for speaker recognition systems is proposed. A conventional approach in which the frame shift is fixed to around half of the frame length may not be the best choice, because the characteristics of the speech signal may rapidly change, especially at phonetic boundaries. Experimental results show that the recognition accuracy increases if the frame interval is directly controlled using phonetic information. By applying these results to the well-known fact that the recognition accuracy is directly correlated with the amount of mutual information, this paper suggests a novel feature frame selection method for speaker recognition. Specifically, feature frames are chosen to have minimum-redundancy within selected feature frames, but maximum-relevancy to speaker models. It is verified by experiments that the proposed method produces consistent improvement, especially in a speaker verification system. It is also robust against variations in acoustic environment.

In this paper, an information theoretical approach to select features for speaker recognition systems is proposed. Conventional approaches having a fixed interval of analysis frames are not appropriate to represent dynamically varying characteristics of speech signals. To maximize the speaker-related information varied by the characteristics of speech signals, we propose an information theory based feature selection method where features are selected to have minimum-redundancy with in selected features but maximum-relevancy to training speaker models. Experimental results verify that the proposed method reduces the error rates of speaker verification systems by 27.37 % in NIST 2002 database.