Explaining the PENTA model: a reply to Arvaniti and Ladd

This paper presents an overview of the Parallel Encoding and Target Approximation (PENTA) model of speech prosody, in response to an extensive critique by Arvaniti & Ladd (2009). PENTA is a framework for conceptually and computationally linking communicative meanings to fine-grained prosodic details, based on an articulatory-functional view of speech. Target Approximation simulates the articulatory realisation of underlying pitch targets – the prosodic primitives in the framework. Parallel Encoding provides an operational scheme that enables simultaneous encoding of multiple communicative functions. We also outline how PENTA can be computationally tested with a set of software tools. With the help of one of the tools, we offer a PENTA-based hypothetical account of the Greek intonational patterns reported by Arvaniti & Ladd, showing how it is possible to predict the prosodic shapes of an utterance based on the lexical and postlexical meanings it conveys

Analyzing Prosody with Legendre Polynomial Coefficients

This investigation demonstrates the effectiveness of Legendre polynomial coefficients representing prosodic contours within the context of two different tasks: nativeness classification and sarcasm detection. By making use of accurate representations of prosodic contours to answer fundamental linguistic questions, we contribute significantly to the body of research focused on analyzing prosody in linguistics as well as modeling prosody for machine learning tasks. Using Legendre polynomial coefficient representations of prosodic contours, we answer prosodic questions about differences in prosody between native English speakers and non-native English speakers whose first language is Mandarin. We also learn more about prosodic qualities of sarcastic speech. We additionally perform machine learning classification for both tasks, (achieving an accuracy of 72.3% for nativeness classification, and achieving 81.57% for sarcasm detection). We recommend that linguists looking to analyze prosodic contours make use of Legendre polynomial coefficients modeling; the accuracy and quality of the resulting prosodic contour representations makes them highly interpretable for linguistic analysis

Context effects on second-language learning of tonal contrasts.

Studies of lexical tone  learning generally focus on monosyllabic contexts, while reports of phonetic learning benefits associated with input variability are based largely on experienced learners. This study trained inexperienced learners on Mandarin tonal contrasts to test two hypotheses regarding the influence of context and variability on tone  learning. The first hypothesis was that increased phonetic variability of tones in disyllabic contexts makes initial tone  learning more challenging in disyllabic than monosyllabic words. The second hypothesis was that the learnability of a given tone varies across contexts due to differences in tonal variability. Results of a word learning experiment supported both hypotheses: tones were acquired less successfully in disyllables than in monosyllables, and the relative difficulty of disyllables was closely related to contextual tonal variability. These results indicate limited relevance of monosyllable-based data on Mandarin learning for the disyllabic majority of the Mandarin lexicon. Furthermore, in the short term, variability can diminish learning; its effects are not necessarily beneficial but dependent on acquisition stage and other learner characteristics. These findings thus highlight the importance of considering contextual variability and the interaction between variability and type of learner in the design, interpretation, and application of research on phonetic learning

L2 Perception and Production of Japanese Lexical Pitch: A Suprasegmental Similarity Account

Adults are known to have difficulties acquiring suprasegmental speech that involves pitch (f0) in a second language (L2) (Graham &amp; Post, 2018; Hirata, 2015; Wang, Spence, Jongman &amp; Sereno, 1999; Wong &amp; Perrachione, 2007). Previous research has suggested that the perceived similarity between L1 and L2 phonology may influence how easily segmental speech is acquired, but this notion of ‘similarity’ may also apply to suprasegmental speech (So &amp; Best, 2010; Wu, Munro &amp; Wang, 2014). In this paper, the L2 acquisition of Japanese lexical pitch was assessed under a ‘Suprasegmental Similarity Account’, which is a theoretical framework inspired by previous models of segmental and suprasegmental speech (Best &amp; Tyler, 2007; Flege, 1995; Mennen, 2015) to account for the L2 acquisition of word prosody. Eight adult native speakers of Japanese and eight adult English-native advanced learners of Japanese participated in a perception and production study of Japanese lexical pitch patterns. Both groups performed similarly in perception, but non-native speakers performed significantly worse in production, particularly for ‘unaccented’ Low–High–High patterns. These findings are discussed in light of the ‘Suprasegmental Similarity Account’.</jats:p

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%

Teaching Lexical Stress: Effective Practice in a Mandarin ELL Context

Current trends in teaching pronunciation to ELLs (English Language Learners) point towards a top-down approach. This refers to putting emphasis on the overarching prosodic features of English rather than the proper pronunciation of consonants and vowels. One of the most integral prosodic features in English is stress. Both lexical stress (stressed syllables within a word) and sentence stress (stressed words within a sentence) play an important role in the prosodic pronunciation of English. However, some languages, such as Mandarin, lack stress in their prosodic systems, instead employing features such as tonality. These languages both have overlap in their fundamental prosodic structures, with pitch changes as integral to both tonality in Mandarin and stress in English. I propose that ESL instructors will instill prosodic skills and thus make better communicators of their students by drawing attention to this positive transfer between both systems

A Method of Teaching English Speaking Learners to Produce Mandarin-Chinese Tones

Learning Mandarin Chinese tones is a big challenge for English speaking learners. The average tonal production accuracy is reported to be about 70 percent for intermediate-level learners and 40 percent for beginning-level Chinese learners. The Chinese tonal proficiency significantly influences the learners\u27 communicative effectiveness, including listening and speaking, but research often overlooks tonal production. This study proposed and tested a novel method of teaching English-speaking learners to pronounce Mandarin Chinese tones. This teaching method includes a Chinese tones bookmark, and a 30--50 minutes in-class training module. The research undertook five cycles of Design-Based Research (DBR) implementations with 31 public school students, adult learners, and Chinese teachers. Two audio recordings, one pre-training and one post-training, were collected and compared through the paired samples t-tests. Interviews, surveys, and class observations were adopted to determine the participants\u27 attitudes toward the training and the teaching model. The results revealed that the designed teaching method was effective to improve the tonal production accuracy of English speaking K-12 children and adult learners. In addition, the results indicated that the participants\u27 attitudes toward the designed method were positive. This study contributes to the current Chinese tonal teaching repertoire and presents a flexible, practical method for teachers to use when instructing students on Chinese tones

Data mining Mandarin tone contour shapes

In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes. We explore the use of data mining and NLP techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech. First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category. Second, we show correlations between these realized contour shape types and a bag of automatically extracted linguistic features. We discuss the implications of the current study within the context of phonological and information theory

Fundamental frequency modelling: an articulatory perspective with target approximation and deep learning

Current statistical parametric speech synthesis (SPSS) approaches typically aim at state/frame-level acoustic modelling, which leads to a problem of frame-by-frame independence. Besides that, whichever learning technique is used, hidden Markov model (HMM), deep neural network (DNN) or recurrent neural network (RNN), the fundamental idea is to set up a direct mapping from linguistic to acoustic features. Although progress is frequently reported, this idea is questionable in terms of biological plausibility. This thesis aims at addressing the above issues by integrating dynamic mechanisms of human speech production as a core component of F0 generation and thus developing a more human-like F0 modelling paradigm. By introducing an articulatory F0 generation model – target approximation (TA) – between text and speech that controls syllable-synchronised F0 generation, contextual F0 variations are processed in two separate yet integrated stages: linguistic to motor, and motor to acoustic. With the goal of demonstrating that human speech movement can be considered as a dynamic process of target approximation and that the TA model is a valid F0 generation model to be used at the motor-to-acoustic stage, a TA-based pitch control experiment is conducted first to simulate the subtle human behaviour of online compensation for pitch-shifted auditory feedback. Then, the TA parameters are collectively controlled by linguistic features via a deep or recurrent neural network (DNN/RNN) at the linguistic-to-motor stage. We trained the systems on a Mandarin Chinese dataset consisting of both statements and questions. The TA-based systems generally outperformed the baseline systems in both objective and subjective evaluations. Furthermore, the amount of required linguistic features were reduced first to syllable level only (with DNN) and then with all positional information removed (with RNN). Fewer linguistic features as input with limited number of TA parameters as output led to less training data and lower model complexity, which in turn led to more efficient training and faster synthesis