Coordination of Speech Recognition Devices in Intelligent Environments with Multiple Responsive Devices
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AuthorBenítez-Guijarro, Antonio; Callejas Carrión, Zoraida; Noguera García, Manuel; Benghazi Akhlaki, Kawtar
Human-computer interactionSpoken interactionSpeech recognitionAmbient intelligenceCoordination of devices
Proceedings 2019, 31, 54; doi:10.3390/proceedings2019031054
SponsorshipThis research has received funding by the project DEP2015-70980-R of the Spanish Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (ERDF), the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823907 (‘Mental health monitoring through interactive conversations’, MENHIR Project), as well as, received inputs from the COST Action IC1303 AAPELE
Devices with oral interfaces are enabling new interesting interaction scenarios and ways of interaction in ambient intelligence settings. The use of several of such devices in the same environment opens up the possibility to compare the inputs gathered from each one of them and perform a more accurate recognition and processing of user speech. However, the combination of multiple devices presents coordination challenges, as the processing of one voice signal by different speech processing units may result in conflicting outputs and it is necessary to decide which is the most reliable source. This paper presents an approach to rank several sources of spoken input in multi-device environments in order to give preference to the input with the highest estimated quality. The voice signals received by the multiple devices are assessed in terms of their calculated acoustic quality and the reliability of the speech recognition hypotheses produced. After this assessment, each input is assigned a unique score that allows the audio sources to be ranked so as to pick the best to be processed by the system. In order to validate this approach, we have performed an evaluation using a corpus of 4608 audios recorded in a two-room intelligent environment with 24 microphones. The experimental results show that our ranking approach makes it possible to successfully orchestrate an increasing number of acoustic inputs, obtaining better recognition rates than considering a single input, both in clear and noisy settings.