Developing automatic articulation, phonation and accent assessment techniques for speakers treated for advanced head and neck cancer

• Articulation model outperforms average human and features relate to tongue position.
• Voice quality model is competitive and features relate to pronunciation variation.
• Accent model is competitive and features relate to phonation and resonance.
• Models show variable success tracking change in perceptual scores over time.

PurposeTo develop automatic assessment models for assessing the articulation, phonation and accent of speakers with head and neck cancer (Experiment 1) and to investigate whether the models can track changes over time (Experiment 2).MethodSeveral speech analysis methods for extracting a compact acoustic feature set that characterizes a speaker’s speech are investigated. The effectiveness of a feature set for assessing a variable is assessed by feeding it to a linear regression model and by measuring the mean difference between the outputs of that model for a set of recordings and the corresponding perceptual scores for the assessed variable (Experiment 1). The models are trained and tested on recordings of 55 speakers treated non-surgically for advanced oral cavity, pharynx and larynx cancer. The perceptual scores are average unscaled ratings of a group of 13 raters. The ability of the models to track changes in perceptual scores over time is also investigated (Experiment 2).ResultsExperiment 1 has demonstrated that combinations of feature sets generally result in better models, that the best articulation model outperforms the average human rater’s performance and that the best accent and phonation models are deemed competitive. Scatter plots of computed and observed scores show, however, that especially low perceptual scores are difficult to assess automatically. Experiment 2 has shown that the articulation and phonation models show only variable success in tracking trends over time and for only one of the time pairs are they deemed compete with the average human rater (Experiment 2). Nevertheless, there is a significant level of agreement between computed and observed trends when considering only a coarse classification of the trend into three classes: clearly positive, clearly negative and minor differences.ConclusionsA baseline tool to support the multi-dimensional evaluation of speakers treated non-surgically for advanced head and neck cancer now exists. More work is required to further improve the models, particularly with respect to their ability to assess low-quality speech.