@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 {Benzeghiba2007Automatic,
	title = {Automatic speech recognition and speech variability: A review},
	journal = {Speech Communication},
	volume = {49},
	number = {10-11},
	year = {2007},
	month = {October},
	pages = {763{\textendash}786},
	abstract = {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.},
	keywords = {asr, bioacoustics, review, speechrecognition},
	issn = {01676393},
	doi = {10.1016/j.specom.2007.02.006},
	url = {http://dx.doi.org/10.1016/j.specom.2007.02.006},
	author = {Benzeghiba, M. and Demori, R. and Deroo, O. and Dupont, S. and Erbes, T. and Jouvet, D. and Fissore, L. and Laface, P. and Mertins, A. and Ris, C.}
}
@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}
}
@article {Barker1999Sinewave,
	title = {Is the sine-wave speech cocktail party worth attending?},
	journal = {Speech Communication},
	volume = {27},
	number = {3-4},
	year = {1999},
	month = {April},
	pages = {159{\textendash}174},
	abstract = {Listeners are remarkably adept at recognising speech that has undergone extensive spectral reduction. Natural speech can be reproduced using as few as three time-varying sinusoids mimicking the corresponding speech formants. Untrained listeners are able to transcribe this \&lsquo;sine-wave{\textquoteright} speech with a high degree of reliability. Coherent phonetic percepts generated by sine-wave speech occur despite an apparent lack of those cues on which low level auditory grouping processes are believed to operate. Consequently, it has been proposed that speech perception is governed by processes operating independently of those described by auditory scene analysis. This paper re-examines the evidence provided by previous perceptual studies of sine-wave speech and presents new data, from perceptual studies employing stimuli constructed from simultaneous sine-wave wave speech sources. These new studies suggest that in conditions that are closer to those of everyday listening, grouping cues have an important role in the formation of coherent speech percepts. In conjunction with these perceptual studies, results from automatic segregation and recognition tasks suggest that sine-wave speech contains sufficient low level, non speech-specific, structure to allow partial descriptions of sine-wave sources to be recovered from two-source mixtures. It is argued that these partial descriptions may be sufficient to account for the limited intelligibility observed in two-source sine-wave speech listening tests.},
	keywords = {asr, bioacoustics, partyeffect, recognition},
	doi = {10.1016/S0167-6393(98)00081-8},
	url = {http://dx.doi.org/10.1016/S0167-6393(98)00081-8},
	author = {Barker, Jon and Cooke, Martin}
}
