Vocabulary size influences spontaneous speech in native language users: Validating the use of automatic speech recognition in individual differences research

Previous research has shown that vocabulary size affects performance on laboratory word production tasks. Individuals who know many words show faster lexical access and retrieve more words belonging to pre-specified categories than individuals who know fewer words. The present study examined the relationship between receptive vocabulary size and speaking skills as assessed in a natural sentence production task. We asked whether measures derived from spontaneous responses to every-day questions correlate with the size of participants’ vocabulary. Moreover, we assessed the suitability of automatic speech recognition for the analysis of participants’ responses in complex language production data. We found that vocabulary size predicted indices of spontaneous speech: Individuals with a larger vocabulary produced more words and had a higher speech-silence ratio compared to individuals with a smaller vocabulary. Importantly, these relationships were reliably identified using manual and automated transcription methods. Taken together, our results suggest that spontaneous speech elicitation is a useful method to investigate natural language production and that automatic speech recognition can alleviate the burden of labor-intensive speech transcription

Towards an Automatic Dictation System for Translators: the TransTalk Project

Professional translators often dictate their translations orally and have them typed afterwards. The TransTalk project aims at automating the second part of this process. Its originality as a dictation system lies in the fact that both the acoustic signal produced by the translator and the source text under translation are made available to the system. Probable translations of the source text can be predicted and these predictions used to help the speech recognition system in its lexical choices. We present the results of the first prototype, which show a marked improvement in the performance of the speech recognition task when translation predictions are taken into account.Comment: Published in proceedings of the International Conference on Spoken Language Processing (ICSLP) 94. 4 pages, uuencoded compressed latex source with 4 postscript figure

Sperry Univac speech communications technology

Technology and systems for effective verbal communication with computers were developed. A continuous speech recognition system for verbal input, a word spotting system to locate key words in conversational speech, prosodic tools to aid speech analysis, and a prerecorded voice response system for speech output are described

Brain Feedback and Adaptive Resonance in Speech Perception

The brain contains ubiquitous reciprocal bottom-up and top-down intercortical and thalamocortical pathways. These resonating feedback pathways may be essential for stable learning of speech and language codes and for context-sensitive selection and completion of noisy speech sounds and word groupings. Context-sensitive speech data, notably interword backward effects in time, have been quantitatively modeled using these concepts but not with purely feedforward models.</jats:p

Lexical and sub-lexical knowledge influences the encoding, storage, and articulation of nonwords

Nonword repetition (NWR) has been used extensively in the study of child language. Although lexical and sub-lexical knowledge is known to influence NWR performance, there has been little examination of the NWR processes (e.g., encoding, storage, articulation) that may be affected by lexical and sub-lexical knowledge. We administered 2- and 3-syllable spoken nonword recognition and nonword repetition tests on two independent groups of 31 children (M=5;07). Spoken nonword recognition primarily involves encoding and storage, whereas NWR involves an additional articulation process. The influence of lexical and sub-lexical knowledge was determined by examining the amount of lexical errors produced. There was a clear involvement of long-term lexical and sub-lexical knowledge in both spoken nonword recognition and NWR. In spoken nonword recognition, twice as many errors involved selecting a foil that contained a lexical item (e.g., yashukup) over a foil that contained only nonsense syllables (e.g., yashunup). In repetition, over 30% of errors changed a nonsense syllable to a lexical item. Our results show that long-term lexical and sub-lexical knowledge is pervasive in NWR – any explanation of NWR performance must therefore consider the influence of lexical and sub-lexical knowledge throughout the whole repetition process, from the encoding of nonwords to the articulation of them