Frédéric Aman

Speech recognition of aged voice in the AAL context: Detection of distress sentences

By 2050, about a third of the French population will be over 65. In the context of technologies development aiming at helping aged people to live independently at home, the CIRDO project aims at implementing an ASR system into a social inclusion product designed for elderly people in order to detect distress situations. Speech recognition systems present higher word error rate when speech is uttered by elderly speakers compared to when non-aged voice is considered. Two specialized corpora in French, AD80 and ERES38, were recorded in this framework by aged people, they were used first to study the possibility of adaptation of standard ASR to aged voice. Then we looked at whether the variability of the WER between speakers could be correlated with the level of dependence. Then, we assessed the performance of distress sentence detection by a filter and we demonstrated a significant drop in performance for those with the lowest degree of autonomy.

Development of Automatic Speech Recognition Techniques for Elderly Home Support: Applications and Challenges

Vocal command may have considerable advantages in terms of usability in the AAL domain. However, efficient audio analysis in smart home environment is a challenging task in large part because of bad speech recognition results in the case of elderly people. Dedicated speech corpora were recorded and employed to adapted generic speech recognizers to this type of population. Evaluation results of a first experiment allowed to draw conclusions about the distress call detection. A second experiments involved participants who played fall scenarios in a realistic smart home, 67 % of the distress calls were detected online. These results show the difficulty of the task and serve as basis to discuss the stakes and the challenges of this promising technology for AAL.

HRI in an ecological dynamic experiment: The GEE corpus based approach for the Emox robot

As part of a human-robot interaction project, the gestural modality is one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot, our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context where the robot is the referee. These gestures are compared with explicitly produced gestures to determine a relevant ontology of gestures. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the “socio-affective glue” dynamics.

Influence of Expressive Speech on ASR Performances: Application to Elderly Assistance in Smart Home

Smart homes are discussed as a win-win solution for maintaining the Elderly at home as a better alternative to care homes for dependent elderly people. Such Smart homes are characterized by rich domestic commands devoted to elderly safety and comfort. The vocal command has been identified as an efficient, well accepted, interaction way, it can be directly addressed to the “habitat”, or through a robotic interface. In daily use, the challenges of vocal commands recognition are the noisy environment but moreover the reformulation and the expressive change of the strictly authorized commands. This paper focuses (1) to show, on the base of elicited corpus, that expressive speech, in particular distress speech, strongly affects generic state of the art ASR systems (20 to 30 %) (2) how interesting improvement thanks to ASR adaptation can regulate (15 %) this degradation. We conclude on the necessary adaptation of ASR system to expressive speech when they are designed for person’s assistance.