Abstract
This paper describes several ways of acoustic keywords spotting (KWS), based on Gaussian mixture model (GMM) hidden Markov models (HMM) and phoneme posterior probabilities from FeatureNet. Context-independent and dependent phoneme models are used in the GMM/HMM system. The systems were trained and evaluated on informal continuous speech. We used different complexities of KWS recognition network and different types of phoneme models. We study the impact of these parameters on the accuracy and computational complexity, an conclude that phoneme posteriors outperform conventional GMM/HMM system.
This work was partially supported by EC project Augmented Multi-party Interaction (AMI), No. 506811 and Grant Agency of Czech Republic under project No. 102/05/0278. Jan Černocký was supported by post-doctoral grant of Grant Agency of Czech Republic No. GA102/02/D108.
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Szöke, I., Schwarz, P., Matějka, P., Burget, L., Karafiát, M., Černocký, J. (2005). Phoneme Based Acoustics Keyword Spotting in Informal Continuous Speech. In: Matoušek, V., Mautner, P., Pavelka, T. (eds) Text, Speech and Dialogue. TSD 2005. Lecture Notes in Computer Science(), vol 3658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551874_39
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DOI: https://doi.org/10.1007/11551874_39
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