Phonetic Realisation and Phonemic Categorisation of the Final Reduced Corner Vowels in the Finnic Languages of Ingria

Individual variability in sound change was explored at three stages of final vowel reduction and loss in the endangered Finnic varieties of Ingria (subdialects of Ingrian, Votic and Ingrian Finnish). The correlation between the realisation of reduced vowels and their phonemic categorisation by speakers was studied. The correlated results showed that if V was pronounced > 70%, its starting loss was not yet perceived, apart from certain frequent elements, but after > 70% loss, V was not perceived any more. A split of 50/50 between V and loss in production correlated with the same split in categorisation. At the beginning of a sound change, production is, therefore, more innovative, but after reanalysis, categorisation becomes more innovative and leads the change. The vowel a was the most innovative in terms of loss, u/o were the most conservative, and i was in the middle, while consonantal palatalisation was more salient than labialisation. These differences are based on acoustics, articulation and perception

Experiments on Detection of Voiced Hesitations in Russian Spontaneous Speech

The development and popularity of voice-user interfaces made spontaneous speech processing an important research field. One of the main focus areas in this field is automatic speech recognition (ASR) that enables the recognition and translation of spoken language into text by computers. However, ASR systems often work less efficiently for spontaneous than for read speech, since the former differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are an important feature in human-human communication and at the same time they are a challenging obstacle for the speech processing tasks. In this paper we address an issue of voiced hesitations (filled pauses and sound lengthenings) detection in Russian spontaneous speech by utilizing different machine learning techniques, from grid search and gradient descent in rule-based approaches to such data-driven ones as ELM and SVM based on the automatically extracted acoustic features. Experimental results on the mixed and quality diverse corpus of spontaneous Russian speech indicate the efficiency of the techniques for the task in question, with SVM outperforming other methods

More than Words: Cross-Linguistic Exploration of Parkinson’s Disease Identification from Speech

This study investigates the effect of listeners’ first language and expertise on their perception of speech produced by people with Parkinson’s Disease (PD). We compared assessment scores and identification accuracy of expert and non-expert Czech and Dutch listeners on two tasks: perception of speech healthiness and recognition of sentence type intonation. We collected speech data from 30 Dutch speakers diagnosed with PD and 30 Dutch speaking healthy controls. Short phrases from intonation tasks and spontaneous monologues were used as stimuli in an online perception experiment. 40 people (20 expert and 20 non-expert listeners) participated. Results show that both expertise and language familiarity are important factors in perception of speech of people with PD, however differences in identification accuracy depend on the task type. In recognition of PD speech, there are prominent acoustic cues that trigger perception of “unhealthiness” in the non-expert listeners, while experience with phonetics may lead to a different focus in their perception of such cues. In intonation accuracy recognition, both Czech and Dutch expert groups outperform both non-expert groups, indicating the added value of phonetic experience for the prosodic task. Yet, there is a clear benefit for having Dutch as the first language for both tasks, as Dutch listeners performed more accurately in both healthiness and sentence type recognition tasks