This paper presents an experimental comparison of various leading vocoders for the application of HMM-based laughter synthesis. Four vocoders, commonly used in HMM-based speech synthesis, are used in copy-synthesis and HMM-based synthesis of both male and female laughter. Subjective evaluations are conducted to assess the performance of the vocoders. The results show that all vocoders perform relatively well in copy-synthesis. In HMM-based laughter synthesis using original phonetic transcriptions, all synthesized laughter voices were significantly lower in quality than copy-synthesis, indicating a challenging task and room for improvements. Interestingly, two vocoders using rather simple and robust excitation modeling performed the best, indicating that robustness in speech parameter extraction and simple parameter representation in statistical modeling are key factors in successful laughter synthesis.

This paper investigates the effect of MPEG audio compression on HMM-based speech synthesis using two state-of-the-art vocoders. Speech signals are first encoded with various compression rates and analyzed using the GlottHMM and STRAIGHT vocoders. Objective evaluation results show that the parameters of both vocoders gradually degrade with increasing compression rates, but with a clear increase in degradation with bit-rates of 32 kbit/s or less. Experiments with HMM-based synthesis with the two vocoders show that the degradation in quality is already perceptible with bit-rates of 32 kbit/s and both vocoders show similar trend in degradation with respect to compression ratio. The most perceptible artefacts induced by the compression are spectral distortion and reduced bandwidth, while prosody is better preserved.

In this paper, we describe a project that explores a novel experi-mental setup towards building a spoken, multi-modally rich, and human-like multiparty tutoring robot. A human-robotinteraction setup is designed, and a human-human dialogue corpus is collect-ed. The corpus targets the development of a dialogue system platform to study verbal and nonverbaltutoring strategies in mul-tiparty spoken interactions with robots which are capable of spo-ken dialogue. The dialogue task is centered on two participants involved in a dialogueaiming to solve a card-ordering game. Along with the participants sits a tutor (robot) that helps the par-ticipants perform the task, and organizes and balances their inter-action. Differentmultimodal signals captured and auto-synchronized by different audio-visual capture technologies, such as a microphone array, Kinects, and video cameras, were coupled with manual annotations. These are used build a situated model of the interaction based on the participants personalities, their state of attention, their conversational engagement and verbal domi-nance, and how that is correlated with the verbal and visual feed-back, turn-management, and conversation regulatory actions gen-erated by the tutor. Driven by the analysis of the corpus, we will show also the detailed design methodologies for an affective, and multimodally rich dialogue system that allows the robot to meas-ure incrementally the attention states, and the dominance for each participant, allowing the robot head Furhat to maintain a well-coordinated, balanced, and engaging conversation, that attempts to maximize the agreement and the contribution to solve the task. This project sets the first steps to explore the potential of us-ing multimodal dialogue systems to build interactive robots that can serve in educational, team building, and collaborative task solving applications.

This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutor’s behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants’ temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.

This project explores a novel experimental setup towards building spoken, multi-modally rich, and human-like multiparty tutoring agent. A setup is developed and a corpus is collected that targets the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions with embodied agents. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. With the participants sits a tutor that helps the participants perform the task and organizes and balances their interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies were coupled with manual annotations to build a situated model of the interaction based on the participants personalities, their temporally-changing state of attention, their conversational engagement and verbal dominance, and the way these are correlated with the verbal and visual feedback, turn-management, and conversation regulatory actions generated by the tutor. At the end of this chapter we discuss the potential areas of research and developments this work opens and some of the challenges that lie in the road ahead.

In this paper, the effect of MPEG audio compression on HMMbased speech synthesis is studied. Speech signals are encoded with various compression rates and analyzed using the GlottHMM vocoder. Objective evaluation results show that the vocoder parameters start to degrade from encoding with bitrates of 32 kbit/s or less, which is also confirmed by the subjective evaluation of the vocoder analysis-synthesis quality. Experiments with HMM-based speech synthesis show that the subjective quality of a synthetic voice trained with 32 kbit/s speech is comparable to a voice trained with uncompressed speech, but lower bit rates induce clear degradation in quality.

Majority of the current voice conversion methods do not focus on the modelling local variations of pitch contour, but only on linear modification of the pitch values, based on means and standard deviations. However, a significant amount of speaker related information is also present in pitch contour. In this paper we propose a non-linear pitch modification method for mapping the pitch contours of the source speaker according to the target speaker pitch contours. This work is done within the framework of Artificial Neural Networks (ANNs) based voice conversion. The pitch contours are represented with Discrete Cosine Transform (DCT) coefficients at the segmental level. The results evaluated using subjective and objective measures confirm that the proposed method performed better in mimicking the target speaker's speaking style when compared to the linear modification method.