The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

Time and information in perceptual adaptation to speech

Presubmission manuscript and supplementary files (stimuli, stimulus presentation code, data, data analysis code).Perceptual adaptation to a talker enables listeners to efficiently resolve the many-to-many mapping between variable speech acoustics and abstract linguistic representations. However, models of speech perception have not delved into the variety or the quantity of information necessary for successful adaptation, nor how adaptation unfolds over time. In three experiments using speeded classification of spoken words, we explored how the quantity (duration), quality (phonetic detail), and temporal continuity of talker-specific context contribute to facilitating perceptual adaptation to speech. In single- and mixed-talker conditions, listeners identified phonetically-confusable target words in isolation or preceded by carrier phrases of varying lengths and phonetic content, spoken by the same talker as the target word. Word identification was always slower in mixed-talker conditions than single-talker ones. However, interference from talker variability decreased as the duration of preceding speech increased but was not affected by the amount of preceding talker-specific phonetic information. Furthermore, efficiency gains from adaptation depended on temporal continuity between preceding speech and the target word. These results suggest that perceptual adaptation to speech may be understood via models of auditory streaming, where perceptual continuity of an auditory object (e.g., a talker) facilitates allocation of attentional resources, resulting in more efficient perceptual processing.NIH NIDCD (R03DC014045

Optimizing the Learning Order of Chinese Characters Using a Novel Topological Sort Algorithm

We present a novel algorithm for optimizing the order in which Chinese characters are learned, one that incorporates the benefits of learning them in order of usage frequency and in order of their hierarchal structural relationships. We show that our work outperforms previously published orders and algorithms. Our algorithm is applicable to any scheduling task where nodes have intrinsic differences in importance and must be visited in topological order