What is the status of schwa in Tashlhiyt? How does it pattern at the segmental and suprasegmental levels? This paper studies these questions by examining the frequencies with which schwa is inserted in different segmental contexts, and by exploring the nature of the interaction between schwa insertion and prosodic prominence. The results show the coexistence of two types of schwa in the language: a transitional schwa and a prosodically triggered schwa. The former is ignored by the phonological system of the language; its occurrence depends solely on the phonetic characteristics of the adjacent consonants. The latter surfaces whenever a prosodically prominent feature does not attach to a vowel or sonorant already present within the word. Both schwas are argued to be manifestations of a more general principle governing the coordination pattern of consonant sequences in Tashlhiyt.

This paper investigates vowel elision and morpheme deletion in Embosi (Bantu C25), an under-resourced language spoken in the Republic of Congo. We propose that the observed morpheme deletion is morphological, and that vowel elision is phonological. The study focuses on vowel elision that occurs across word boundaries between the contact of long/short vowels (i.e. CV[long] # V[short].CV), and between the contact of short/short vowels (CV[short] # V[short].CV). Several different categories of morphemes are explored: (i) prepositions (ya, mo), (ii) class-noun nominal prefixes (ba, etc.), (iii) singular subject pronouns (ngá, nO, wa). For example, the preposition, ya, regularly deletes allowing for vowel elision if vowel contact occurs between the head of the noun phrase and the previous word. Phonetically motivated speech variants are proposed in the lexicon used for forced alignment (segmentation) enabling these phenomena to be quantified in the corpus so as to develop a dictionary containing relevant phonetic variants.

The leveling of contour tones in Thai, uttered in a continuous context, has served as a natural point of difficulty for tone recognition experiments. Two tone recognition experiments presented here both include five lexical Thai tones (high, mid, low, rising, and falling) as abstract Bayesian models incorporated into a multi-model Hidden Markov Model. The HMM was developed using Thai natural language utterances to test its performance in correctly identifying Thai lexical tone categories. All utterances used for testing and training were produced in a laboratory setting. Utterances for the first experiment were produced in a citation context, and utterances for the second experiment were produced in a continuous context. The results of the two experiments were compared to test if the context had a significant effect on correctly identifying tone category. Findings showed the context of the utterance had a significant effect on the HMM’s ability to correctly identify tone category, F(1,48) = 5.82, p = 0.020. The identification of the correct tone category for citation utterances showed a significant increase in performance than for the correct identification of tone category for continuous utterances, t(48) = 5.38, p < 0.001.

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