Language is a system of discrete and abstract elements. Yet, rarely (if ever) can we identify predictable, linear, and/or clear one-to-one relationships between the speech signal and linguistic categories. Rather, the relationship between speech and language consists of fuzzy boundaries between categories and myriad sources of ambiguity. This ambiguity has often been considered as no more than noisy data arising from equipment error, recording conditions that are less than ideal, population under-sampling, or other sources of spurious behavior in the data. However, upon further inspection, it has been proposed that ambiguity may play a crucial role in the development, evolution, and employment of language itself: listeners benefit from variability when learning phonological categories and generalizing from them, ambiguity of the source of acoustic effects serves as a catalyst of sound change actuation, speakers adapt their productions when the environment may make their speech ambiguous to listeners, and gradiency in linguistic representations may allow greater flexibility for listeners to adjust to cross-speaker and cross-context variation.
The current research era presents opportunities for tackling this difficult topic in ways that have never before been possible or in some cases even imaginable. Recent trends and techniques involving co-registration of multiple data streams allow us to disentangle the articulatory source of observable acoustic effects of vocal tract dynamics, in spite of complicated many-to-one or even many-to-many articulatory-acoustic mappings. The interdisciplinary and trans-global collaborative research that is becoming increasingly popular in our virtual age encourages a wide range of interpretations and strategies for dealing with ambiguous data. Cutting edge machine learning techniques and statistical approaches can help dis-ambiguate fuzzy data patterns to uncover meaningful underlying structure. Virtual experiment platforms that have flourished in recent times can be used to collect participant response data at a scale that was previously unthinkable, allowing novel insight into group-level patterns that characterize the cognitive processing of potentially ambiguous speech signals.
Rather than consider the ambiguous relationship between speech and language as mere noise or even avoid it entirely in study design and the interpretation of study results, we invite original research papers that highlight ambiguity itself as a central aspect of the research. Possible themes within the topic of ambiguity in speech production and perception include but are not limited to: many-to-one and many-to-many mappings in speech production, the role of acoustic/perceptual ambiguity in sound change processes, perceptual cue-weighting and cue-trading, resolving visual ambiguity in signed or spoken language, adaptation to speech in noise or to speech perturbation, cognitive processing of speech at the borders shared between phonological categories, and computational and statistical techniques for resolving data ambiguity in speech signals.
Language is a system of discrete and abstract elements. Yet, rarely (if ever) can we identify predictable, linear, and/or clear one-to-one relationships between the speech signal and linguistic categories. Rather, the relationship between speech and language consists of fuzzy boundaries between categories and myriad sources of ambiguity. This ambiguity has often been considered as no more than noisy data arising from equipment error, recording conditions that are less than ideal, population under-sampling, or other sources of spurious behavior in the data. However, upon further inspection, it has been proposed that ambiguity may play a crucial role in the development, evolution, and employment of language itself: listeners benefit from variability when learning phonological categories and generalizing from them, ambiguity of the source of acoustic effects serves as a catalyst of sound change actuation, speakers adapt their productions when the environment may make their speech ambiguous to listeners, and gradiency in linguistic representations may allow greater flexibility for listeners to adjust to cross-speaker and cross-context variation.
The current research era presents opportunities for tackling this difficult topic in ways that have never before been possible or in some cases even imaginable. Recent trends and techniques involving co-registration of multiple data streams allow us to disentangle the articulatory source of observable acoustic effects of vocal tract dynamics, in spite of complicated many-to-one or even many-to-many articulatory-acoustic mappings. The interdisciplinary and trans-global collaborative research that is becoming increasingly popular in our virtual age encourages a wide range of interpretations and strategies for dealing with ambiguous data. Cutting edge machine learning techniques and statistical approaches can help dis-ambiguate fuzzy data patterns to uncover meaningful underlying structure. Virtual experiment platforms that have flourished in recent times can be used to collect participant response data at a scale that was previously unthinkable, allowing novel insight into group-level patterns that characterize the cognitive processing of potentially ambiguous speech signals.
Rather than consider the ambiguous relationship between speech and language as mere noise or even avoid it entirely in study design and the interpretation of study results, we invite original research papers that highlight ambiguity itself as a central aspect of the research. Possible themes within the topic of ambiguity in speech production and perception include but are not limited to: many-to-one and many-to-many mappings in speech production, the role of acoustic/perceptual ambiguity in sound change processes, perceptual cue-weighting and cue-trading, resolving visual ambiguity in signed or spoken language, adaptation to speech in noise or to speech perturbation, cognitive processing of speech at the borders shared between phonological categories, and computational and statistical techniques for resolving data ambiguity in speech signals.