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This paper presents the adaptation of HMM-based speech synthesis to laughter signals. Acoustic laughter synthesis HMMs are built with only 3 minutes of laughter data. An evaluation experiment shows that the method achieves significantly better performance than previous works. In addition, the first method to generate laughter phonetic transcriptions from high-level signals (in our case, arousal signals) is described. This enables to generate new laughter phonetic sequences, that do not exist in the original data. The generated phonetic sequences are used as input for HMM synthesis and reach similar perceived naturalness as laughs synthesized from existing phonetic transcriptions. These methods open promising perspectives for the integration of natural laughs in man-machine interfaces. It could also be used for other vocalizations (sighs, cries, coughs, etc.). [ABSTRACT FROM PUBLISHER]

This paper focuses on the automatic modification of the degree of articulation (hypo and hyperarticulation) of an existing standard neutral voice in the framework of HMM-based speech synthesis. Hypo and hyperarticulation refer to the production of speech respectively with a reduction and an increase of the articulatory efforts compared to the neutral style. Starting from a source speaker for which neutral, hypo and hyperarticulated speech data are available, statistical transformations are computed during the adaptation of the neutral speech synthesizer. These transformations are then applied to a new target speaker for which no hypo or hyperarticulated recordings are available. Four statistical methods are investigated, differing in the speaking style adaptation technique (model-space Linear Scaling LS vs. CMLLR) and in the speaking style transposition approach (phonetic vs. acoustic correspondence) they use. The efficiency of these techniques is assessed for the transposition of prosody and of filter coefficients separately. Besides we investigate which representation of the spectral envelope is the most suited for this purpose: MGC, LSP, PARCOR and LAR coefficients. Subjective evaluations are performed in order to determine which statistical transformation method achieves the highest performance in terms of segmental quality, reproduction of the articulation degree and speaker identity preservation. The most successful method is finally used for automatically modifying the degree of articulation of existing standard neutral voices. [ABSTRACT FROM PUBLISHER]

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance. [ABSTRACT FROM AUTHOR]

Abstract: HMM-based speech synthesis is very convenient for creating a synthesizer whose speaker characteristics and speaking styles can be easily modified. This can be obtained by adapting a source speaker's model to a target speaker's model, using intra-speaker voice adaptation techniques. In this paper, we focus on high-quality HMM-based speech synthesis integrating various degrees of articulation, and more specifically on the internal mechanisms leading to the perception of the degrees of articulation by listeners. Therefore the process of adapting a neutral speech synthesizer to generate hypo and hyperarticulated speech is broken down into four factors: cepstrum, prosody, phonetic transcription adaptation as well as the complete adaptation. The impact of these factors on the perceived degree of articulation is studied. Moreover, this study is complemented with an Absolute Category Rating (ACR) evaluation, allowing the subjective assessment of hypo/hyperarticulated speech through various dimensions: comprehension, non-monotony, fluidity and pronunciation. This paper quantifies the importance of prosody and cepstrum adaptation as well as the use of a Natural Language Processor able to generate realistic hypo and hyperarticulated phonetic transcriptions. [Copyright &y& Elsevier]

Abstract: Hypo and hyperarticulation refer to the production of speech with respectively a reduction and an increase of the articulatory efforts compared to the neutral style. Produced consciously or not, these variations of articulatory efforts depend upon the surrounding environment, the communication context and the motivation of the speaker with regard to the listener. The goal of this work is to integrate hypo and hyperarticulation into speech synthesizers, such that they are more realistic by automatically adapting their way of speaking to the contextual situation, like humans do. Based on our preliminary work, this paper provides a thorough and detailed study on the analysis and synthesis of hypo and hyperarticulated speech. It is divided into three parts. In the first one, we focus on both acoustic and phonetic modifications due to articulatory effort changes. The second part aims at developing a HMM-based speech synthesizer allowing a continuous control of the degree of articulation. This requires to first tackle the issue of speaking style adaptation to derive hypo and hyperarticulated speech from the neutral synthesizer. Once this is done, an interpolation and extrapolation of the resulting models enables to finely tune the voice so that it is generated with the desired articulatory efforts. Finally the third and last part focuses on a perceptual study of speech with a variable articulation degree, where it is analyzed how intelligibility and various other voice dimensions are affected. [Copyright &y& Elsevier]

Abstract: Source-tract decomposition (or glottal flow estimation) is one of the basic problems of speech processing. For this, several techniques have been proposed in the literature. However, studies comparing different approaches are almost nonexistent. Besides, experiments have been systematically performed either on synthetic speech or on sustained vowels. In this study we compare three of the main representative state-of-the-art methods of glottal flow estimation: closed-phase inverse filtering, iterative and adaptive inverse filtering, and mixed-phase decomposition. These techniques are first submitted to an objective assessment test on synthetic speech signals. Their sensitivity to various factors affecting the estimation quality, as well as their robustness to noise are studied. In a second experiment, their ability to label voice quality (tensed, modal, soft) is studied on a large corpus of real connected speech. It is shown that changes of voice quality are reflected by significant modifications in glottal feature distributions. Techniques based on the mixed-phase decomposition and on a closed-phase inverse filtering process turn out to give the best results on both clean synthetic and real speech signals. On the other hand, iterative and adaptive inverse filtering is recommended in noisy environments for its high robustness. [Copyright &y& Elsevier]