Using stress to disambiguate spoken Thai sentences containing syntactic ambiguity

Abstract

We h a v e developed a Bayesian classier to determine whether syllables in connected Thai speech are weakly or strongly stressed by using ve acoustic parameters: syllable rhyme duration, mean F0, F 0 standard deviation, mean energy, and the standard deviation of the energy. With speaker-dependent data normalization, we a c hieved a classication accuracy of 99%. The classication accuracy drops to 96% when we used speaker-independent normalization. We h a v e also developed prosodic constraints that can use this stress information to syntactically disambiguate a class of ambiguous sentences that arise from the use of compounding in spoken Thai.

Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

“…In recent years, ABSA has experienced notable advancements, particularly with the integration of rule-based methods in the NLP domain [14]. Researchers have proposed various approaches for sentiment analysis that leverage predefined linguistic rules based on opinion lexicons, syntactic and semantic indications, such as parts-of-speech (POS) tags and lexical indicators [15]. POS tags serve to identify the grammatical category of each word, while lexical indications service in recognizing sentiment expressions.…”

Cluster Analysis of Prominent Features for Determining Stress Levels in Thai Speech

“…Many researchers have studied the acoustic correlation of stress in several languages [7,16,17]. The correlation appears to vary from language to language.…”

“…Stress is assumed to be a feature of a syllable, but for practical purposes, stress can be attributed to the vowel [23]. Many researches used vowel duration for representing stressed/unstressed syllables [7,17,23,24] but some researchers proposed to use the rhyme duration for representing them [13,16,25].…”