HIFI-AV: An Audio-visual Corpus for Spoken Language Human-Machine Dialogue Research in Spanish

In this paper, we describe a new multi-purpose audio-visual database on the context of speech interfaces for controlling household electronic devices. The database comprises speech and video recordings of 19 speakers interacting with a HIFI audio box by means of a spoken dialogue system. Dialogue management is based on Bayesian Networks and the system is provided with contextual information handling strategies. Each speaker was requested to fulfil different sets of specific goals following predefined scenarios, according to both different complexity levels and degrees of freedom or initiative allowed to the user. Due to a careful design and its size, the recorded database allows comprehensive studies on speech recognition, speech understanding, dialogue modeling and management, microphone array based speech processing, and both speech and video-based acoustic source localisation. The database has been labelled for quality and efficiency studies on dialogue performance. The whole database has been validated through both objective and subjective tests

NEMOHIFI: An Affective HiFi Agent

This demo concerns a recently developed prototype of an emotionally-sensitive autonomous HiFi Spoken Conversa- tional Agent, called NEMOHIFI. The baseline agent was developed by the Speech Technology Group (GTH) and has recently been integrated with an emotional engine called NEMO (Need-inspired Emotional Model) to enable it to adapt to users emotion and respond to the users using ap- propriate expressive speech. NEMOHIFI controls and man- ages the HiFi audio system, and for end users, its functions equate a remote control, except that instead of clicking, the user interacts with the agent using voice. A pairwise com- parison between the baseline (non-adaptive) and NEMO- HIFI is also presented

Textless Speech Emotion Conversion using Discrete & Decomposed Representations

International audienceSpeech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity. In this study, we cast the problem of emotion conversion as a spoken language translation task. We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion. First, we modify the speech content by translating the phoneticcontent units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder. Such a paradigm allows us to go beyond spectral and parametric changes of the signal, and model non-verbal vocalizations, such as laughter insertion, yawning removal, etc. We demonstrate objectively and subjectively that the proposed method is vastly superior to current approaches and even beats text-based systems in terms of perceived emotion and audio quality. We rigorously evaluate all components of such a complex system and conclude with an extensive model analysis and ablation study to better emphasize the architectural choices, strengths and weaknesses of the proposed method. Samples are available under the following link: https: //speechbot.github.io/emotion

Emotion on the Road—Necessity, Acceptance, and Feasibility of Affective Computing in the Car

Besides reduction of energy consumption, which implies alternate actuation and light construction, the main research domain in automobile development in the near future is dominated by driver assistance and natural driver-car communication. The ability of a car to understand natural speech and provide a human-like driver assistance system can be expected to be a factor decisive for market success on par with automatic driving systems. Emotional factors and affective states are thereby crucial for enhanced safety and comfort. This paper gives an extensive literature overview on work related to influence of emotions on driving safety and comfort, automatic recognition, control of emotions, and improvement of in-car interfaces by affect sensitive technology. Various use-case scenarios are outlined as possible applications for emotion-oriented technology in the vehicle. The possible acceptance of such future technology by drivers is assessed in a Wizard-Of-Oz user study, and feasibility of automatically recognising various driver states is demonstrated by an example system for monitoring driver attentiveness. Thereby an accuracy of 91.3% is reported for classifying in real-time whether the driver is attentive or distracted