A Hidden Conditional Random Field-Based Approach for Thai Tone Classification

In Thai, tonal information is a crucial component for identifying the lexical meaning of a word. Consequently, Thai tone classification can obviously improve performance of Thai speech recognition system. In this article, we therefore reported our study of Thai tone classification. Based on our investigation, most of Thai tone classification studies relied on statistical machine learning approaches, especially the Artificial Neural Network (ANN)-based approach and the Hidden Markov Model (HMM)-based approach. Although both approaches gave reasonable performances, they had some limitations due to their mathematical models. We therefore introduced a novel approach for Thai tone classification using a Hidden Conditional Random Field (HCRF)-based approach. In our study, we also investigated tone configurations involving tone features, frequency scaling and normalization techniques in order to fine tune performances of Thai tone classification. Experiments were conducted in both isolated word scenario and continuous speech scenario. Results showed that the HCRF-based approach with the feature F_dF_aF, ERB-rate scaling and a z-score normalization technique yielded the highest performance and outperformed a baseline using the ANN-based approach, which had been reported as the best for the Thai tone classification, in both scenarios. The best performance of HCRF-based approach provided the error rate reduction of 10.58% and 12.02% for isolated word scenario and continuous speech scenario respectively when comparing with the best result of baselines

Tone classification of syllable -segmented Thai speech based on multilayer perceptron

Thai is a monosyllabic and tonal language. Thai makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has not only to recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system.;In this study, a tone classification of syllable-segmented Thai speech which incorporates the effects of tonal coarticulation, stress and intonation was developed. Automatic syllable segmentation, which performs the segmentation on the training and test utterances into syllable units, was also developed. The acoustical features including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables were used as the main discriminating features. A multilayer perceptron (MLP) trained by backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%

Deep learning assessment of syllable affiliation of intervocalic consonants

In English, a sentence like “He made out our intentions.” could be misperceived as “He may doubt our intentions.” because the coda /d/ sounds like it has become the onset of the next syllable. The nature and occurrence condition of this resyllabification phenomenon are unclear, however. Previous empirical studies mainly relied on listener judgment, limited acoustic evidence, such as voice onset time, or average formant values to determine the occurrence of resyllabification. This study tested the hypothesis that resyllabification is a coarticulatory reorganisation that realigns the coda consonant with the vowel of the next syllable. Deep learning in conjunction with dynamic time warping (DTW) was used to assess syllable affiliation of intervocalic consonants. The results suggest that convolutional neural network- and recurrent neural network-based models can detect cases of resyllabification using Mel-frequency spectrograms. DTW analysis shows that neural network inferred resyllabified sequences are acoustically more similar to their onset counterparts than their canonical productions. A binary classifier further suggests that, similar to the genuine onsets, the inferred resyllabified coda consonants are coarticulated with the following vowel. These results are interpreted with an account of resyllabification as a speech-rate-dependent coarticulatory reorganisation mechanism in speech

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination

Predicting tonal realizations in one Chinese dialect from another

Pronunciation dictionaries are usually expensive and time-consuming to prepare for the computational modeling of human languages, especially when the target language is under-resourced. Northern Chinese dialects are often under-resourced but used by a significant number of speakers. They share the basic sound inventories with Standard Chinese (SC). Also, their words usually share the segmental realizations and logographic written forms with the SC translation equivalents. Hence the pronunciation dictionaries of northern Chinese dialects could be easily available if we were able to predict the tonal realizations of the dialect words from the tonal information of their SC counterparts. This paper applies statistical modeling to investigate the tonal aspect of the related words between a northern dialect, i.e. Jinan Mandarin (JM), and Standard Chinese (SC). Multi-linear regression models were built with between-word pitch distance of JM words as the dependent variable and the following were included as the predictors: SC tonal relations, between-dialect tonal identity, and individual backgrounds. The results showed that tonal relations in SC and between-dialect identity, as predictors featuring the relation between the JM and SC tonal systems, are significant and robust predictors of JM tonal realizations. The speakers’ sociolinguistic and cognitive backgrounds, together with the tonal merge and neutral tone information within JM, are important for the prediction of JM tonal realizations and affect the way that between-language predictors take effect

Uncovering the myth of learning to read Chinese characters: phonetic, semantic, and orthographic strategies used by Chinese as foreign language learners

Oral Session - 6A: Lexical modeling: no. 6A.3Chinese is considered to be one of the most challenging orthographies to be learned by non-native speakers, in particular, the character. Chinese character is the basic reading unit that converges sound, form and meaning. The predominant type of Chinese character is semantic-phonetic compound that is composed of phonetic and semantic radicals, giving the clues of the sound and meaning, respectively. Over the last two decades, psycholinguistic research has made significant progress in specifying the roles of phonetic and semantic radicals in character processing among native Chinese speakers …postprin

(Dis)connections between specific language impairment and dyslexia in Chinese

Poster Session: no. 26P.40Specific language impairment (SLI) and dyslexia describe language-learning impairments that occur in the absence of a sensory, cognitive, or psychosocial impairment. SLI is primarily defined by an impairment in oral language, and dyslexia by a deficit in the reading of written words. SLI and dyslexia co-occur in school-age children learning English, with rates ranging from 17% to 75%. For children learning Chinese, SLI and dyslexia also co-occur. Wong et al. (2010) first reported on the presence of dyslexia in a clinical sample of 6- to 11-year-old school-age children with SLI. The study compared the reading-related cognitive skills of children with SLI and dyslexia (SLI-D) with 2 groups of children …postprin