Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results

Children\u27s Sensitivity to Pitch Variation in Language

Children acquire consonant and vowel categories by 12 months, but take much longer to learn to interpret perceptible variation. This dissertation considers children’s interpretation of pitch variation. Pitch operates, often simultaneously, at different levels of linguistic structure. English-learning children must disregard pitch at the lexical level—since English is not a tone language—while still attending to pitch for its other functions. Chapters 1 and 5 outline the learning problem and suggest ways children might solve it. Chapter 2 demonstrates that 2.5-year-olds know pitch cannot differentiate words in English. Chapter 3 finds that not until age 4–5 do children correctly interpret pitch cues to emotions. Chapter 4 demonstrates some sensitivity between 2.5 and 5 years to the pitch cue to lexical stress, but continuing difficulties at the older ages. These findings suggest a late trajectory for interpretation of prosodic variation; throughout, I propose explanations for this protracted time-course

Input quality and speech perception development in bilingual infants' first year of life

Epub 2021 Oct 20Individual differences in infants’ native phonological development have been linked to the quantity and quality of infant-directed speech (IDS). The effects of parental and infant bilingualism on this relation in 131 five-and nine-month- old monolingual and bilingual Spanish and Basque infants (72 male; 59 female; from white middle-class background) were investigated. Bilingualism did not affect the developmental trajectory of infants’ native and non-native speech perception and the quality of maternal speech. In both language groups, vowel exaggeration in IDS was significantly related to speech perception skills for 9-month- olds (r = −.30), but not for 5-month- olds. This demonstrates that bilingual and monolingual caregivers provide their infants with speech input that assists their task of learning the phonological inventory of one or two languages.Eusko Jaurlaritza, Grant/Award Number: BERC 2018-2021; Severo Ochoa Excellence Program, Grant/Award Number: SEV-2015- 0490; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-105528GA- I00; H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: 79890