Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge.Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.

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Uncertainty propagation has been successfully employed for speech recognition in nonstationary noise environments. The uncertainty about the features is typically represented as a diagonal covariance matrix for static features only. We present a framework for estimating the uncertainty over both static and dynamic features as a full covariance matrix. The estimated covariance matrix is then multiplied by scaling coefficients optimized on development data. We achieve 21% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding without uncertainty, that is five times more than the reduction achieved with diagonal uncertainty covariance for static features only.

This paper investigates the use of hidden Markov models (HMM) for Modern Standard Arabic speech synthesis. HMM-based speech synthesis systems require a description of each speech unit with a set of contextual features that specifies phonetic, phonological and linguistic aspects. To apply this method to Arabic language, a study of its particularities was conducted to extract suitable contextual features. Two phenomena are highlighted: vowel quantity and gemination. This work focuses on how to model geminated consonants (resp. long vowels), either considering them as fully-fledged phonemes or as the same phonemes as their simple (resp. short) counterparts but with a different duration. Four modelling approaches have been proposed for this purpose. Results of subjective and objective evaluations show that there is no important difference between differentiating modelling units associated to geminated consonants (resp. long vowels) from modelling units associated to simple consonants (resp. short vowels) and merging them as long as gemination and vowel quantity information is included in the set of features.