Abstract
This paper presents the performance achieved using Confidence Measures (CM) in Automatic Speech Recognition (ASR) for the transcription of weather reports from the Spanish public broadcast channel (RTVE). In the CM computation, first Acoustic-Phonetic Decoding (APD) is carried out, then we align reference and hypothesis word sequences through a phone-graph, and finally in this decoding mesh given a time interval, the maximum posterior probability of the hypothesized word is selected as the CM value. The final goal is to use the CM module as an extension of the ASR system to automatically evaluate the reliability of recognition results, discarding low confidence words at the output. These CM can be used as a tool for Unsupervised Learning Techniques, and also for helping human supervision of recognition results. If accurate enough, these CM would increase the usability as well as the robustness of speech applications.
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Olcoz, J., Ortega, A., Miguel, A., Lleida, E. (2014). Confidence Measures in Automatic Speech Recognition Systems for Error Detection in Restricted Domains. In: Navarro Mesa, J.L., et al. Advances in Speech and Language Technologies for Iberian Languages. Lecture Notes in Computer Science(), vol 8854. Springer, Cham. https://doi.org/10.1007/978-3-319-13623-3_18
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DOI: https://doi.org/10.1007/978-3-319-13623-3_18
Publisher Name: Springer, Cham
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