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

Explaining the PENTA mode: A reply to Arvaniti and Ladd (2009)

his 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

Automatic prosodic analysis for computer aided pronunciation teaching

Correct pronunciation of spoken language requires the appropriate modulation of acoustic characteristics of speech to convey linguistic information at a suprasegmental level. Such prosodic modulation is a key aspect of spoken language and is an important component of foreign language learning, for purposes of both comprehension and intelligibility. Computer aided pronunciation teaching involves automatic analysis of the speech of a non-native talker in order to provide a diagnosis of the learner's performance in comparison with the speech of a native talker. This thesis describes research undertaken to automatically analyse the prosodic aspects of speech for computer aided pronunciation teaching. It is necessary to describe the suprasegmental composition of a learner's speech in order to characterise significant deviations from a native-like prosody, and to offer some kind of corrective diagnosis. Phonological theories of prosody aim to describe the suprasegmental composition of speech..

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

Prosody in text-to-speech synthesis using fuzzy logic

For over a thousand years, inventors, scientists and researchers have tried to reproduce human speech. Today, the quality of synthesized speech is not equivalent to the quality of real speech. Most research on speech synthesis focuses on improving the quality of the speech produced by Text-to-Speech (TTS) systems. The best TTS systems use unit selection-based concatenation to synthesize speech. However, this method is very timely and the speech database is very large. Diphone concatenated synthesized speech requires less memory, but sounds robotic. This thesis explores the use of fuzzy logic to make diphone concatenated speech sound more natural. A TTS is built using both neural networks and fuzzy logic. Text is converted into phonemes using neural networks. Fuzzy logic is used to control the fundamental frequency for three types of sentences. In conclusion, the fuzzy system produces f0 contours that make the diphone concatenated speech sound more natural

The perception of lexical stress in German: effects of segmental duration and vowel quality in different prosodic patterns

Several decades of research, focusing on English, Dutch and German, have set up a hierarchy of acoustic properties for cueing lexical stress. It attributes the strongest cue to criterial-level f0 change, followed by duration, but low weight to energy and to stressed-vowel spectra. This paper re-examines the established view with new data from German. In the natural productions of the German word pair Kaffee 'coffee' - Café 'locality' (with initial vs. final stress in a North German pronunciation), vowel duration was manipulated in a complementary fashion across the two syllables in five steps, spanning the continuum from initial to final stress on each word. The two base words provided different vowel qualities as the second variable, the intervocalic fricative was varied in two values, long and short, taken from Café and Kaffee, and the generated test words were inserted in a low f0 tail and in a high f0 hat-pattern plateau, which both eliminated f0 change as a cue to lexical stress. The sentence stimuli were judged in two listening experiments by 16 listeners in each as to whether the first or the second syllable of the test word was stressed. The results show highly significant effects of vowel duration, vowel quality and fricative duration. The combined vowel-quality and fricative variable can outweigh vowel duration as a cue to lexical stress. The effect of the prosodic frame is only marginal, especially related to a rhythmic factor. The paper concludes that there is no general hierarchy with a fixed ranking of the variables traditionally adduced to signal lexical stress. Every prosodic embedding of segmental sequences defines the hierarchy afresh