On recognition of non-native speech using probabilistic lexical model
Marzieh Razavi, Mathew Magimai.-Doss · 2014
Despite various advances in automatic speech recognition (ASR) technology, recognition of speech uttered by non-native speakers is still a challenging problem.In this paper, we investigate the role of different factors such as type of lexical model and choice of acoustic units in recognition of speech uttered by non-native speakers.More precisely, we investigate the influence of the probabilistic lexical model in the framework of Kullback-Leibler divergence based hidden Markov model (KL-HMM) approach in handling pronunciation variabilities by comparing it against hybrid HMM/artificial neural network (ANN) approach where the lexical model is deterministic.Moreover, we study the effect of acoustic units (being context-independent or clustered context-dependent phones) on ASR performance in both KL-HMM and hybrid HMM/ANN frameworks.Our experimental studies on French part of Me-diaParl as a bilingual corpus indicate that the probabilistic lexical modeling approach in the KL-HMM framework can capture the pronunciation variations present in non-native speech effectively.More precisely, the experimental results show that the KL-HMM system using context-dependent acoustic units and trained solely on native speech data can lead to better ASR performance than adaptation techniques such as maximum likelihood linear regression.