Reduction of intelligibility went along with large increases of early peak responses M50TRF, but strongly reduced responses in M200TRF. Our TRF analysis yielded marked temporally differential effects of vocoding: ∼50-110 ms (M50TRF), ∼175-230 ms (M200TRF), and ∼315-380 ms (M350TRF). In addition, we used inter-related facets of neural speech tracking (e.g., speech envelope reconstruction, speech-brain coherence, and components of broadband coherence spectra) to endorse our findings in TRFs. In the present MEG study, we exploited temporal response functions (TRFs), which has been used to describe the time course of speech tracking on a gradient from intelligible to unintelligible degraded speech. However, the temporal dynamics of neural speech tracking and their relation to speech intelligibility are not clear. Neural speech tracking of degraded speech has been used to advance the understanding of how brain processes and speech intelligibility are interrelated. Listening to speech with poor signal quality is challenging. These findings show the importance of including multiple linguistic levels in the analysis of learner discourse and have implications for a more holistic and functionally based approach to language instruction. Dimension 1, for example, identifies correlates of informationally driven discourse on all three linguistic levels under investigation. Results show significant differences between L1 and L2 groups on four of six dimensions and reveal novel patterns of co-occurrence. In this study, we investigate lexico-grammar, fluency, and prosody in LINDSEI (German, Czech, and Spanish) alongside British and American English comparable corpora, using multidimensional analysis, a statistical procedure that identifies co-occurring linguistic features and leads to functional interpretation of the discourse. DOI: 10.1109/ of the characteristics of spoken learner language has increased in recent years but has been primarily limited to the investigation of one linguistic level (e.g., lexico-grammar), which gives a limited picture of learners’ overall linguistic competence (e.g., Skarnitzl & Rumlová, 2019 ). IEEE Transactions on Audio and Electroacoustics, 17, 225–246. IEEE recommended practice for speech quality measurements. For example, file PNM02_01-07.wav is a recording of Pacific Northwest Male #02 reading sentence number 01-07. After an underscore, the sentence identification number comprises the remainder of the filename. The third character indicates talker gender, and the fourth and fifth characters are meaningless digits, serially assigned to talkers during corpus creation. The first two characters in the filenames reflect the dialect region of the talker (PN = Pacific Northwest, NC = Northern Cities). Sentence identification numbers are derived from the “list-sentence” notation of the original IEEE sentence lists: for example, sentence 01-07 corresponds to sentence #7 from list #1 of the original numbering scheme. Individual transcript files for each sentence are also included. Transcripts of the 180 sentences (along with their identification numbers) are included in the corpus in tab-delimited format. The sentence texts are drawn from the IEEE “Harvard” set. They have NOT been checked or corrected by humans (much less by well-trained phoneticians or speech scientists). These are TextGrids for use with the praat software that have been automatically generated by the Penn Phonetics lab forced aligner software and are known to contain misalignments. The set of audio files has been RMS-normalized to equate intensity across all recordings in the corpus.Ī set of 3600 time-aligned transcriptions are included in the corpus. Files are readings of 180 sentences by 20 different talkers (5 males and 5 females from each of two dialect regions of American English: the Pacific Northwest and the Northern Cities). The corpus includes 3600 audio files in WAV format, sampled at 44.1 kHz with 16-bit depth. A., Haywood, J., Gehani, N., & Rudolph, S. You can download the entire corpus (in compressed. All information found here is also contained in the README file included with the corpus. This page contains information about the UW/NC corpus. The University of Washington/Northwestern University (UW/NU) Corpus 1.0
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