Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods

In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker's performance.Comment: Computer Speech and Language, Vol. 81, Article 101515, June 202

The influence of lexical selection disruptions on articulation

Interactive models of language production predict that it should be possible to observe long-distance interactions; effects that arise at one level of processing influence multiple subsequent stages of representation and processing. We examine the hypothesis that disruptions arising in nonform-based levels of planning—specifically, lexical selection—should modulate articulatory processing. A novel automatic phonetic analysis method was used to examine productions in a paradigm yielding both general disruptions to formulation processes and, more specifically, overt errors during lexical selection. This analysis method allowed us to examine articulatory disruptions at multiple levels of analysis, from whole words to individual segments. Baseline performance by young adults was contrasted with young speakers’ performance under time pressure (which previous work has argued increases interaction between planning and articulation) and performance by older adults (who may have difficulties inhibiting nontarget representations, leading to heightened interactive effects). The results revealed the presence of interactive effects. Our new analysis techniques revealed these effects were strongest in initial portions of responses, suggesting that speech is initiated as soon as the first segment has been planned. Interactive effects did not increase under response pressure, suggesting interaction between planning and articulation is relatively fixed. Unexpectedly, lexical selection disruptions appeared to yield some degree of facilitation in articulatory processing (possibly reflecting semantic facilitation of target retrieval) and older adults showed weaker, not stronger interactive effects (possibly reflecting weakened connections between lexical and form-level representations)