Phonetic inventory for an Arabic speech corpus

Corpus design for speech synthesis is a well-researched topic in languages such as English compared to Modern Standard Arabic, and there is a tendency to focus on methods to automatically generate the orthographic transcript to be recorded (usually greedy methods). In this work, a study of Modern Standard Arabic (MSA) phonetics and phonology is conducted in order to create criteria for a greedy meth-od to create a speech corpus transcript for recording. The size of the dataset is reduced a number of times using these optimisation methods with different parameters to yield a much smaller dataset with identical phonetic coverage than before the reduction, and this output transcript is chosen for recording. This is part of a larger work to create a completely annotated and segmented speech corpus for MSA

Data-driven phrasing for speech synthesis in low-resource languages

We present an approach to build phrase break prediction models when synthesizing text in low resource languages. This method allows building models without depending on the availability of part of speech taggers, or corpus with hand annotated breaks. We use the same speech data used for building a synthetic voice, to deduce acoustic phrase breaks. We perform unsupervised part of speech induction over a small text corpus in the language at hand. We use these tags and train a grammar based phrasing model. In this paper, we show results for the languages: English, Portuguese and Marathi, which suggest that we can quickly build very reasonable phrasing models for new languages using very little data

Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations

While there are many Deaf or Hard of Hearing (DHH) individuals with excellent reading literacy, there are also some DHH individuals who have lower English literacy. American Sign Language (ASL) is not simply a method of representing English sentences. It is possible for an individual to be fluent in ASL, while having limited fluency in English. To overcome this barrier, we aim to make it easier to generate ASL animations for websites, through the use of motion-capture data recorded from human signers to build different predictive models for ASL animations; our goal is to automate this aspect of animation synthesis to create realistic animations. This dissertation consists of several parts: Part I, defines key terminology for timing and speed parameters, and surveys literature on prior linguistic and computational research on ASL. Next, the motion-capture data that our lab recorded from human signers is discussed, and details are provided about how we enhanced this corpus to make it useful for speed and timing research. Finally, we present the process of adding layers of linguistic annotation and processing this data for speed and timing research. Part II presents our research on data-driven predictive models for various speed and timing parameters of ASL animations. The focus is on predicting the (1) existence of pauses after each ASL sign, (2) predicting the time duration of these pauses, and (3) predicting the change of speed for each ASL sign within a sentence. We measure the quality of the proposed models by comparing our models with state-of-the-art rule-based models. Furthermore, using these models, we synthesized ASL animation stimuli and conducted a user-based evaluation with DHH individuals to measure the usability of the resulting animation. Finally, Part III presents research on whether the timing parameters individuals prefer for animation may differ from those in recordings of human signers. Furthermore, it also includes research to investigate the distribution of acceleration curves in recordings of human signers and whether utilizing a similar set of curves in ASL animations leads to measurable improvements in DHH users\u27 perception of animation quality