Reduplication and word stress in Vietnamese

In this paper acoustic properties of two types of reduplicative compound (full and tone sandhi reduplications) were examined to show acoustic evidence of assymetrical prominence relations (van der Hulst, 2005), in support of a hypothesis that Vietnamese reduplications are phonetically right headed and that tone sandhi is a reduction phenomenon occurring on prosodically weak positions (Shih, 2005). The results show phonetic evidence of prosodic constituency at the level of the bisyllabic word in Vietnamese.

Cross-language Perception of Word-final Stops by Native Vietnamese Listeners: Preliminary Results on the Role of Specific, Non-native Phonetic Experience

This study examined the discrimination of word-final stop contrasts (/p/-/t/, /p/-/k/, /t/-/k/) in English and Thai by 12 listeners who speak Vietnamese as their first language (L1). Vietnamese shares specific phonetic realization of stops with Thai, i.e., unreleased final stop and differs from English which allows both released and unreleased final stops. These 12 native Vietnamese (NV) listeners’ discrimination accuracy was compared to that of the two listener groups (Australian English (AE), native Thai (NT)) tested in previous studies. The NV group was less accurate than the native group in discriminating both English and Thai stop contrasts. In particular, for the Thai /t/-/k/ contrast, they were significantly less accurate than the AE listeners. The present findings suggest that experience with specific (i.e., unreleased) and native phonetic realization of sounds may be essential in accurate discrimination of final stop contrasts. The effect of L1 dialect on cross-language speech perception is discussed.

Maximum-likelihood noncoherent lattice decoding of QAM

We present a novel, maximum-likelihood (ML), lattice-decoding algorithm for noncoherent block detection of QAM signals. The computational complexity is polynomial in the block length; making it feasible for implementation compared with the exhaustive search ML detector. The algorithm works by enumerating the nearest neighbor regions for a plane defined by the received vector; in a conceptually similar manner to sphere decoding. Simulations show that the new algorithm significantly outperforms existing approaches