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Simulation d'erreurs de reconnaissance automatique dans un cadre de compréhension de la parole

Abstract : Simulating ASR errors for training SLU systems This paper presents an approach to simulate automatic speech recognition (ASR) errors from manual transcriptions and how it can be used to improve the performance of spoken language understanding (SLU) systems. The proposed method is based on the use of both acoustic and linguistic word embeddings in order to define a similarity measure between words. This measure is dedicated to predict ASR confusions. Actually, we assume that words acoustically and linguistically close are the ones confused by an ASR system. Experiments were carried on the French MEDIA corpus focusing on hotel reservation. They show that this approach significantly improves SLU system performance with a relative reduction of 21.2% of concept/value error rate (CVER), particularly when the SLU system is based on a neural approach (reduction of 22.4% of CVER). A comparison to a naive noising approach shows that the proposed noising approach is particularly relevant.
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Submitted on : Monday, November 9, 2020 - 8:29:34 PM
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Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Estève. Simulation d'erreurs de reconnaissance automatique dans un cadre de compréhension de la parole. XXXIIe Journées d'Etudes sur la Parole (JEP 2018), Jun 2018, Aix-en-Provence, France. ⟨hal-01757770⟩

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