@article {VanCompernolle2001Recognizing,
	title = {Recognizing speech of goats, wolves, sheep and {\textellipsis} non-natives},
	journal = {Speech Communication},
	volume = {35},
	number = {1-2},
	year = {2001},
	month = {August},
	pages = {71{\textendash}79},
	abstract = {This paper reviews the current understanding of acoustic{\textendash}phonetic issues and the problems arising when trying to recognize speech from non-native speakers. Conceptually, regional accents are well modeled by systematic shifts in pronunciation. Therefore, simultaneous recognition of multiple regional variants may be performed by using multiple acoustic models in parallel, or by adding pronunciation variants in the dictionary. Recognition of non-native speech is much more difficult because it is influenced both by the native language of the speaker and non-native target language. It is characterized by a much greater speaker variability due to different levels of proficiency. A few language-pair specific transformation rules describing prototypical nativized pronunciations was found to be useful both in general speech recognition and in dedicated applications. However, due to the nature of the errors and the cross-language transformations, non-native speech recognition will remain inherently much harder. Moreover, the trend in speech recognition towards more detailed modeling seems to be counterproductive for the recognition of non-native speech and limits progress in this field.},
	keywords = {asr, bioacoustics, linguistics, transcription},
	issn = {01676393},
	doi = {10.1016/S0167-6393(00)00096-0},
	url = {http://dx.doi.org/10.1016/S0167-6393(00)00096-0},
	author = {Van Compernolle, D.}
}
@article {Kroger2009Towards,
	title = {Towards a neurocomputational model of speech production and perception},
	journal = {Speech Communication},
	volume = {51},
	number = {9},
	year = {2009},
	month = {September},
	pages = {793{\textendash}809},
	abstract = {The limitation in performance of current speech synthesis and speech recognition systems may result from the fact that these systems are not designed with respect to the human neural processes of speech production and perception. A neurocomputational model of speech production and perception is introduced which is organized with respect to human neural processes of speech production and perception. The production{\textendash}perception model comprises an artificial computer-implemented vocal tract as a front-end module, which is capable of generating articulatory speech movements and acoustic speech signals. The structure of the production{\textendash}perception model comprises motor and sensory processing pathways. Speech knowledge is collected during training stages which imitate early stages of speech acquisition. This knowledge is stored in artificial self-organizing maps. The current neurocomputational model is capable of producing and perceiving vowels, VC-, and CV-syllables (V~=~vowels and C~=~voiced plosives). Basic features of natural speech production and perception are predicted from this model in a straight forward way: Production of speech items is feedforward and feedback controlled and phoneme realizations vary within perceptually defined regions. Perception is less categorical in the case of vowels in comparison to consonants. Due to its human-like production{\textendash}perception processing the model should be discussed as a basic module for more technical relevant approaches for high-quality speech synthesis and for high performance speech recognition.},
	keywords = {asr, bioacoustics, linguistics, speech, speechrecognition},
	issn = {01676393},
	doi = {10.1016/j.specom.2008.08.002},
	url = {http://dx.doi.org/10.1016/j.specom.2008.08.002},
	author = {Kr{\"o}ger, Bernd J. and Kannampuzha, Jim and Neuschaefer-Rube, Christiane}
}
